{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/diagnostic/papers/11","list_of":"/task/diagnostic","task":"Diagnostic","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":11,"pages_in_order":46,"rows_per_page":100,"rows":[1001,1100],"of":4513,"counts":{"archive_papers_tagged":4513,"with_a_code_link":1213,"where_syntology_ran_a_sample":157,"not_listed_spam_title":0,"listed":4513,"listed_where_code_ran":157,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":132,"every_run_a_failure_of_syntologys_instrument":25,"listed_with_a_run_with_no_instrument_failure":132,"listed_every_run_a_failure_of_syntologys_instrument":25,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/diagnostic","prev":"/task/diagnostic/papers/10","next":"/task/diagnostic/papers/12","papers":[{"url":"/paper/oxnet-omni-supervised-thoracic-disease","slug":"oxnet-omni-supervised-thoracic-disease","title":"OXnet: Omni-supervised Thoracic Disease Detection from Chest X-rays","date":"2021-04-07","arxiv_id":"2104.03218","repositories_listed":1,"syntology":null},{"url":"/paper/local-metrics-for-multi-object-tracking","slug":"local-metrics-for-multi-object-tracking","title":"Local Metrics for Multi-Object Tracking","date":"2021-04-06","arxiv_id":"2104.02631","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-the-role-of-bert-token","slug":"exploring-the-role-of-bert-token","title":"Exploring the Role of BERT Token Representations to Explain Sentence Probing Results","date":"2021-04-03","arxiv_id":"2104.01477","repositories_listed":1,"syntology":null},{"url":"/paper/alue-arabic-language-understanding-evaluation","slug":"alue-arabic-language-understanding-evaluation","title":"ALUE: Arabic Language Understanding Evaluation","date":"2021-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/diagnosing-vision-and-language-navigation","slug":"diagnosing-vision-and-language-navigation","title":"Diagnosing Vision-and-Language Navigation: What Really Matters","date":"2021-03-30","arxiv_id":"2103.16561","repositories_listed":1,"syntology":null},{"url":"/paper/explaining-a-neural-attention-model-for","slug":"explaining-a-neural-attention-model-for","title":"Explaining a Neural Attention Model for Aspect-Based Sentiment Classification Using Diagnostic Classification","date":"2021-03-29","arxiv_id":"2103.15927","repositories_listed":1,"syntology":null},{"url":"/paper/xprotonet-diagnosis-in-chest-radiography-with","slug":"xprotonet-diagnosis-in-chest-radiography-with","title":"XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations","date":"2021-03-19","arxiv_id":"2103.10663","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/xprotonet-diagnosis-in-chest-radiography-with#ran","syntology_url":"https://syntology.ai/paper/2103.10663","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.10663"}},"official":null}},{"url":"/paper/is-medical-chest-x-ray-data-anonymous","slug":"is-medical-chest-x-ray-data-anonymous","title":"Deep Learning-based Patient Re-identification Is able to Exploit the Biometric Nature of Medical Chest X-ray Data","date":"2021-03-15","arxiv_id":"2103.08562","repositories_listed":1,"syntology":null},{"url":"/paper/longitudinal-quantitative-assessment-of-covid","slug":"longitudinal-quantitative-assessment-of-covid","title":"Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs","date":"2021-03-12","arxiv_id":"2103.07240","repositories_listed":1,"syntology":null},{"url":"/paper/multiple-instance-captioning-learning","slug":"multiple-instance-captioning-learning","title":"Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articles","date":"2021-03-08","arxiv_id":"2103.05121","repositories_listed":1,"syntology":null},{"url":"/paper/autocalibration-and-tweedie-dominance-for","slug":"autocalibration-and-tweedie-dominance-for","title":"Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learning","date":"2021-03-05","arxiv_id":"2103.03635","repositories_listed":1,"syntology":null},{"url":"/paper/towards-evaluating-the-robustness-of-deep","slug":"towards-evaluating-the-robustness-of-deep","title":"Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack","date":"2021-03-05","arxiv_id":"2103.03438","repositories_listed":1,"syntology":null},{"url":"/paper/intrapapillary-capillary-loop-classification","slug":"intrapapillary-capillary-loop-classification","title":"Intrapapillary Capillary Loop Classification in Magnification Endoscopy: Open Dataset and Baseline Methodology","date":"2021-02-19","arxiv_id":"2102.09963","repositories_listed":1,"syntology":null},{"url":"/paper/chexternal-generalization-of-deep-learning","slug":"chexternal-generalization-of-deep-learning","title":"CheXternal: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays and External Clinical Settings","date":"2021-02-17","arxiv_id":"2102.08660","repositories_listed":1,"syntology":null},{"url":"/paper/deep-co-attention-network-for-multi-view","slug":"deep-co-attention-network-for-multi-view","title":"Deep Co-Attention Network for Multi-View Subspace Learning","date":"2021-02-15","arxiv_id":"2102.07751","repositories_listed":1,"syntology":null},{"url":"/paper/reference-based-texture-transfer-for-single","slug":"reference-based-texture-transfer-for-single","title":"Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance images","date":"2021-02-10","arxiv_id":"2102.05450","repositories_listed":1,"syntology":null},{"url":"/paper/single-model-deep-learning-on-imbalanced","slug":"single-model-deep-learning-on-imbalanced","title":"Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification","date":"2021-02-02","arxiv_id":"2102.01284","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-uncertainties-in-electrochemical","slug":"evaluating-uncertainties-in-electrochemical","title":"Evaluating uncertainties in electrochemical impedance spectra of solid oxide fuel cells","date":"2021-01-20","arxiv_id":"2101.08049","repositories_listed":1,"syntology":null},{"url":"/paper/diagnosis-of-intelligent-reflecting-surface","slug":"diagnosis-of-intelligent-reflecting-surface","title":"Diagnosis of Intelligent Reflecting Surface in Millimeter-wave Communication Systems","date":"2021-01-11","arxiv_id":"2101.03792","repositories_listed":1,"syntology":null},{"url":"/paper/a-deep-learning-approach-for-covid-19-viral","slug":"a-deep-learning-approach-for-covid-19-viral","title":"A Deep Learning Approach for COVID-19 & Viral Pneumonia Screening with X-ray Images","date":"2021-01-02","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dvd-a-diagnostic-dataset-for-multi-step","slug":"dvd-a-diagnostic-dataset-for-multi-step","title":"DVD: A Diagnostic Dataset for Multi-step Reasoning in Video Grounded Dialogue","date":"2021-01-01","arxiv_id":"2101.00151","repositories_listed":1,"syntology":null},{"url":"/paper/mri-brain-tumor-segmentation-and-uncertainty","slug":"mri-brain-tumor-segmentation-and-uncertainty","title":"MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architectures","date":"2020-12-30","arxiv_id":"2012.15294","repositories_listed":1,"syntology":null},{"url":"/paper/classification-of-12-lead-ecgs-the-physionet","slug":"classification-of-12-lead-ecgs-the-physionet","title":"Classification of 12-lead ECGs: the PhysioNet/ Computing in Cardiology Challenge 2020","date":"2020-12-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fuzzy-conviction-score-for-discriminating","slug":"fuzzy-conviction-score-for-discriminating","title":"Fuzzy Conviction Score for Discriminating Decision-Tree-Classified Feature Vectors w.r.t. Relative Distances from Decision Boundaries","date":"2020-12-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/covidx-computer-aided-diagnosis-of-covid-19","slug":"covidx-computer-aided-diagnosis-of-covid-19","title":"COVIDX: Computer-aided diagnosis of Covid-19 and its severity prediction with raw digital chest X-ray images","date":"2020-12-25","arxiv_id":"2012.13605","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-and-visualizable-convolutional","slug":"efficient-and-visualizable-convolutional","title":"Efficient and Visualizable Convolutional Neural Networks for COVID-19 Classification Using Chest CT","date":"2020-12-22","arxiv_id":"2012.11860","repositories_listed":1,"syntology":null},{"url":"/paper/graph-evolving-meta-learning-for-low-resource","slug":"graph-evolving-meta-learning-for-low-resource","title":"Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation","date":"2020-12-22","arxiv_id":"2012.11988","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":3,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 3 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/graph-evolving-meta-learning-for-low-resource#ran","syntology_url":"https://syntology.ai/paper/2012.11988","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.11988"}},"official":{"repos":["ha-lins/GEML-MDG"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/classifying-breast-histopathology-images-with","slug":"classifying-breast-histopathology-images-with","title":"Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline","date":"2020-12-11","arxiv_id":"2012.06136","repositories_listed":1,"syntology":null},{"url":"/paper/do-not-repeat-these-mistakes-a-critical","slug":"do-not-repeat-these-mistakes-a-critical","title":"Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies","date":"2020-12-11","arxiv_id":"2012.08333","repositories_listed":1,"syntology":null},{"url":"/paper/ensemble-cvdnet-a-deep-learning-based-end-to","slug":"ensemble-cvdnet-a-deep-learning-based-end-to","title":"Ensemble-CVDNet: A Deep Learning based End-to-End Classification Framework for COVID-19 Detection using Ensembles of Networks","date":"2020-12-09","arxiv_id":"2012.09132","repositories_listed":1,"syntology":null},{"url":"/paper/learning-medical-image-denoising-with-deep","slug":"learning-medical-image-denoising-with-deep","title":"Learning Medical Image Denoising with Deep Dynamic Residual Attention Network","date":"2020-12-09","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/advancing-diagnostic-performance-and-clinical","slug":"advancing-diagnostic-performance-and-clinical","title":"Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization","date":"2020-11-25","arxiv_id":"2011.13011","repositories_listed":1,"syntology":null},{"url":"/paper/large-scale-machine-learning-based","slug":"large-scale-machine-learning-based","title":"Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology","date":"2020-11-25","arxiv_id":"2011.13012","repositories_listed":1,"syntology":null},{"url":"/paper/multiscale-attention-guided-network-for-covid","slug":"multiscale-attention-guided-network-for-covid","title":"Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-ray Images","date":"2020-11-11","arxiv_id":"2012.02278","repositories_listed":1,"syntology":null},{"url":"/paper/noise-conscious-training-of-non-local-neural","slug":"noise-conscious-training-of-non-local-neural","title":"Noise Conscious Training of Non Local Neural Network powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising","date":"2020-11-11","arxiv_id":"2011.05684","repositories_listed":1,"syntology":null},{"url":"/paper/prediction-problems-inspired-by-animal","slug":"prediction-problems-inspired-by-animal","title":"From Eye-blinks to State Construction: Diagnostic Benchmarks for Online Representation Learning","date":"2020-11-09","arxiv_id":"2011.04590","repositories_listed":1,"syntology":null},{"url":"/paper/grading-the-severity-of-arteriolosclerosis","slug":"grading-the-severity-of-arteriolosclerosis","title":"Automated Grading System of Retinal Arterio-venous Crossing Patterns: A Deep Learning Approach Replicating Ophthalmologist's Diagnostic Process of Arteriolosclerosis","date":"2020-11-07","arxiv_id":"2011.03772","repositories_listed":1,"syntology":null},{"url":"/paper/chest-x-ray-image-phase-features-for-improved","slug":"chest-x-ray-image-phase-features-for-improved","title":"Chest X-ray Image Phase Features for Improved Diagnosis of COVID-19 Using Convolutional Neural Network","date":"2020-11-06","arxiv_id":"2011.03585","repositories_listed":1,"syntology":null},{"url":"/paper/deep-transfer-learning-for-automated","slug":"deep-transfer-learning-for-automated","title":"Deep Transfer Learning for Automated Diagnosis of Skin Lesions from Photographs","date":"2020-11-06","arxiv_id":"2011.04475","repositories_listed":1,"syntology":null},{"url":"/paper/from-dataset-recycling-to-multi-property","slug":"from-dataset-recycling-to-multi-property","title":"From Dataset Recycling to Multi-Property Extraction and Beyond","date":"2020-11-06","arxiv_id":"2011.03228","repositories_listed":1,"syntology":null},{"url":"/paper/noise2sim-similarity-based-self-learning-for","slug":"noise2sim-similarity-based-self-learning-for","title":"Suppression of Correlated Noise with Similarity-based Unsupervised Deep Learning","date":"2020-11-06","arxiv_id":"2011.03384","repositories_listed":1,"syntology":null},{"url":"/paper/recommendations-for-bayesian-hierarchical","slug":"recommendations-for-bayesian-hierarchical","title":"Recommendations for Bayesian hierarchical model specifications for case-control studies in mental health","date":"2020-11-03","arxiv_id":"2011.01725","repositories_listed":1,"syntology":null},{"url":"/paper/the-gap-on-gap-tackling-the-problem-of","slug":"the-gap-on-gap-tackling-the-problem-of","title":"The Gap on GAP: Tackling the Problem of Differing Data Distributions in Bias-Measuring Datasets","date":"2020-11-03","arxiv_id":"2011.01837","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-gap-on-gap-tackling-the-problem-of#ran","syntology_url":"https://syntology.ai/paper/2011.01837","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.01837"}},"official":{"repos":["vid-koci/weightingGAP"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-closer-look-at-linguistic-knowledge-in","slug":"a-closer-look-at-linguistic-knowledge-in","title":"A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English","date":"2020-11-02","arxiv_id":"2011.00960","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-contrast-the-counterfactual","slug":"learning-to-contrast-the-counterfactual","title":"Learning to Contrast the Counterfactual Samples for Robust Visual Question Answering","date":"2020-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/covid-fact-a-fully-automated-capsule-network","slug":"covid-fact-a-fully-automated-capsule-network","title":"COVID-FACT: A Fully-Automated Capsule Network-based Framework for Identification of COVID-19 Cases from Chest CT scans","date":"2020-10-30","arxiv_id":"2010.16041","repositories_listed":1,"syntology":null},{"url":"/paper/ct-caps-feature-extraction-based-automated","slug":"ct-caps-feature-extraction-based-automated","title":"CT-CAPS: Feature Extraction-based Automated Framework for COVID-19 Disease Identification from Chest CT Scans using Capsule Networks","date":"2020-10-30","arxiv_id":"2010.16043","repositories_listed":1,"syntology":null},{"url":"/paper/mmft-bert-multimodal-fusion-transformer-with","slug":"mmft-bert-multimodal-fusion-transformer-with","title":"MMFT-BERT: Multimodal Fusion Transformer with BERT Encodings for Visual Question Answering","date":"2020-10-27","arxiv_id":"2010.14095","repositories_listed":1,"syntology":null},{"url":"/paper/kvasir-instrument-diagnostic-and-therapeutic","slug":"kvasir-instrument-diagnostic-and-therapeutic","title":"Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy","date":"2020-10-23","arxiv_id":"2011.08065","repositories_listed":1,"syntology":null},{"url":"/paper/matching-the-clinical-reality-accurate-oct","slug":"matching-the-clinical-reality-accurate-oct","title":"Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels","date":"2020-10-23","arxiv_id":"2010.12316","repositories_listed":1,"syntology":null},{"url":"/paper/machine-learning-for-the-diagnosis-of","slug":"machine-learning-for-the-diagnosis-of","title":"Machine learning for the diagnosis of Parkinson's disease: A systematic review","date":"2020-10-13","arxiv_id":"2010.06101","repositories_listed":1,"syntology":null},{"url":"/paper/learning-which-features-matter-roberta","slug":"learning-which-features-matter-roberta","title":"Learning Which Features Matter: RoBERTa Acquires a Preference for Linguistic Generalizations (Eventually)","date":"2020-10-11","arxiv_id":"2010.05358","repositories_listed":1,"syntology":null},{"url":"/paper/sickle-cell-disease-diagnosis-support","slug":"sickle-cell-disease-diagnosis-support","title":"Sickle-cell disease diagnosis support selecting the most appropriate machinelearning method: Towards a general and interpretable approach for cellmorphology analysis from microscopy images","date":"2020-10-09","arxiv_id":"2010.04511","repositories_listed":1,"syntology":null},{"url":"/paper/synthesising-clinically-realistic-chest-x","slug":"synthesising-clinically-realistic-chest-x","title":"Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs","date":"2020-10-07","arxiv_id":"2010.03975","repositories_listed":1,"syntology":null},{"url":"/paper/mh-covidnet-diagnosis-of-covid-19-using-deep","slug":"mh-covidnet-diagnosis-of-covid-19-using-deep","title":"MH-COVIDNet: Diagnosis of COVID-19 using Deep Neural Networks and Meta-heuristic-based Feature Selection on X-ray Images","date":"2020-10-06","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-diagnostic-study-of-explainability","slug":"a-diagnostic-study-of-explainability","title":"A Diagnostic Study of Explainability Techniques for Text Classification","date":"2020-09-25","arxiv_id":"2009.13295","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"0 ran · 3 unverified","sample_list":"/paper/a-diagnostic-study-of-explainability#ran","syntology_url":"https://syntology.ai/paper/2009.13295","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.13295"}},"official":{"repos":["copenlu/xai-benchmark"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/federated-learning-for-computational","slug":"federated-learning-for-computational","title":"Federated Learning for Computational Pathology on Gigapixel Whole Slide Images","date":"2020-09-21","arxiv_id":"2009.10190","repositories_listed":1,"syntology":null},{"url":"/paper/manipulation-robust-regression-discontinuity","slug":"manipulation-robust-regression-discontinuity","title":"Manipulation-Robust Regression Discontinuity Designs","date":"2020-09-16","arxiv_id":"2009.07551","repositories_listed":1,"syntology":null},{"url":"/paper/rcnn-for-region-of-interest-detection-in","slug":"rcnn-for-region-of-interest-detection-in","title":"RCNN for Region of Interest Detection in Whole Slide Images","date":"2020-09-16","arxiv_id":"2009.07532","repositories_listed":1,"syntology":null},{"url":"/paper/a-mobile-app-for-wound-localization-using","slug":"a-mobile-app-for-wound-localization-using","title":"A Mobile App for Wound Localization using Deep Learning","date":"2020-09-15","arxiv_id":"2009.07133","repositories_listed":1,"syntology":null},{"url":"/paper/accelerating-covid-19-differential-diagnosis","slug":"accelerating-covid-19-differential-diagnosis","title":"Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis","date":"2020-09-13","arxiv_id":"2009.06116","repositories_listed":1,"syntology":null},{"url":"/paper/convolution-neural-networks-for-diagnosing","slug":"convolution-neural-networks-for-diagnosing","title":"Convolution Neural Networks for diagnosing colon and lung cancer histopathological images","date":"2020-09-08","arxiv_id":"2009.03878","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-pathology-segmentation-with","slug":"semi-supervised-pathology-segmentation-with","title":"Semi-supervised Pathology Segmentation with Disentangled Representations","date":"2020-09-05","arxiv_id":"2009.02564","repositories_listed":1,"syntology":null},{"url":"/paper/deep-hypergraph-u-net-for-brain-graph","slug":"deep-hypergraph-u-net-for-brain-graph","title":"Deep Hypergraph U-Net for Brain Graph Embedding and Classification","date":"2020-08-30","arxiv_id":"2008.13118","repositories_listed":1,"syntology":null},{"url":"/paper/skyline-interactive-in-editor-computational","slug":"skyline-interactive-in-editor-computational","title":"Skyline: Interactive In-Editor Computational Performance Profiling for Deep Neural Network Training","date":"2020-08-15","arxiv_id":"2008.06798","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/skyline-interactive-in-editor-computational#ran","syntology_url":"https://syntology.ai/paper/2008.06798","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.06798"}},"official":{"repos":["skylineprof/skyline"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/renal-cell-carcinoma-detection-and-subtyping","slug":"renal-cell-carcinoma-detection-and-subtyping","title":"Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images","date":"2020-08-12","arxiv_id":"2008.05332","repositories_listed":1,"syntology":null},{"url":"/paper/3d-flat-feasible-learned-acquisition","slug":"3d-flat-feasible-learned-acquisition","title":"3D FLAT: Feasible Learned Acquisition Trajectories for Accelerated MRI","date":"2020-08-11","arxiv_id":"2008.04808","repositories_listed":1,"syntology":null},{"url":"/paper/enhance-cnn-robustness-against-noises-for","slug":"enhance-cnn-robustness-against-noises-for","title":"Enhance CNN Robustness Against Noises for Classification of 12-Lead ECG with Variable Length","date":"2020-08-08","arxiv_id":"2008.03609","repositories_listed":1,"syntology":null},{"url":"/paper/confidence-guided-lesion-mask-based","slug":"confidence-guided-lesion-mask-based","title":"Confidence-guided Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images in Patients with Post-treatment Malignant Gliomas","date":"2020-08-06","arxiv_id":"2008.02859","repositories_listed":1,"syntology":null},{"url":"/paper/guiding-cnns-towards-relevant-concepts-by","slug":"guiding-cnns-towards-relevant-concepts-by","title":"Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization","date":"2020-08-04","arxiv_id":"2008.01478","repositories_listed":1,"syntology":null},{"url":"/paper/on-transferability-of-histological-tissue","slug":"on-transferability-of-histological-tissue","title":"On Transferability of Histological Tissue Labels in Computational Pathology","date":"2020-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-learning-of-particle-image","slug":"unsupervised-learning-of-particle-image","title":"Unsupervised Learning of Particle Image Velocimetry","date":"2020-07-28","arxiv_id":"2007.14487","repositories_listed":1,"syntology":null},{"url":"/paper/pan-cancer-computational-histopathology-pc","slug":"pan-cancer-computational-histopathology-pc","title":"Pan-Cancer Computational Histopathology (PC-CHiP) analysis using deep learning","date":"2020-07-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cvr-net-a-deep-convolutional-neural-network","slug":"cvr-net-a-deep-convolutional-neural-network","title":"CVR-Net: A deep convolutional neural network for coronavirus recognition from chest radiography images","date":"2020-07-21","arxiv_id":"2007.11993","repositories_listed":1,"syntology":null},{"url":"/paper/visual-explanation-for-identification-of-the","slug":"visual-explanation-for-identification-of-the","title":"Visual Explanation for Identification of the Brain Bases for Dyslexia on fMRI Data","date":"2020-07-17","arxiv_id":"2007.09260","repositories_listed":1,"syntology":null},{"url":"/paper/hardware-implementation-of-deep-network","slug":"hardware-implementation-of-deep-network","title":"Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications","date":"2020-07-11","arxiv_id":"2007.05657","repositories_listed":1,"syntology":null},{"url":"/paper/non-image-data-classification-with","slug":"non-image-data-classification-with","title":"Classification with 2-D Convolutional Neural Networks for breast cancer diagnosis","date":"2020-07-07","arxiv_id":"2007.03218","repositories_listed":1,"syntology":null},{"url":"/paper/collaborative-unsupervised-domain-adaptation-1","slug":"collaborative-unsupervised-domain-adaptation-1","title":"Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis","date":"2020-07-05","arxiv_id":"2007.07222","repositories_listed":1,"syntology":null},{"url":"/paper/covxnet-a-multi-dilation-convolutional-neural","slug":"covxnet-a-multi-dilation-convolutional-neural","title":"CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sacnn-self-attention-convolutional-neural","slug":"sacnn-self-attention-convolutional-neural","title":"SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/primary-tumor-origin-classification-of-lung","slug":"primary-tumor-origin-classification-of-lung","title":"Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning","date":"2020-06-30","arxiv_id":"2006.16633","repositories_listed":1,"syntology":null},{"url":"/paper/chexpert-approximating-the-chexpert-labeler","slug":"chexpert-approximating-the-chexpert-labeler","title":"CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Output","date":"2020-06-26","arxiv_id":"2006.15229","repositories_listed":1,"syntology":null},{"url":"/paper/lesion-mask-based-simultaneous-synthesis-of","slug":"lesion-mask-based-simultaneous-synthesis-of","title":"Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GAN","date":"2020-06-26","arxiv_id":"2006.14761","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-based-computational-pathology","slug":"deep-learning-based-computational-pathology","title":"Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary","date":"2020-06-24","arxiv_id":"2006.13932","repositories_listed":1,"syntology":null},{"url":"/paper/mining-misdiagnosis-patterns-from-biomedical","slug":"mining-misdiagnosis-patterns-from-biomedical","title":"Mining Misdiagnosis Patterns from Biomedical Literature","date":"2020-06-24","arxiv_id":"2006.13721","repositories_listed":1,"syntology":null},{"url":"/paper/rtex-a-novel-methodology-for-ranking-tagging","slug":"rtex-a-novel-methodology-for-ranking-tagging","title":"RTEX: A novel methodology for Ranking, Tagging, and Explanatory diagnostic captioning of radiography exams","date":"2020-06-11","arxiv_id":"2006.06316","repositories_listed":1,"syntology":null},{"url":"/paper/optilime-optimized-lime-explanations-for","slug":"optilime-optimized-lime-explanations-for","title":"OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms","date":"2020-06-10","arxiv_id":"2006.05714","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/optilime-optimized-lime-explanations-for#ran","syntology_url":"https://syntology.ai/paper/2006.05714","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.05714"}},"official":{"repos":["giorgiovisani/lime_stability"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/vaes-in-the-presence-of-missing-data","slug":"vaes-in-the-presence-of-missing-data","title":"VAEs in the Presence of Missing Data","date":"2020-06-09","arxiv_id":"2006.05301","repositories_listed":1,"syntology":null},{"url":"/paper/dinucleotide-repeats-in-coronavirus-sars-cov","slug":"dinucleotide-repeats-in-coronavirus-sars-cov","title":"Dinucleotide repeats in coronavirus SARS-CoV-2 genome: evolutionary implications","date":"2020-05-30","arxiv_id":"2006.00280","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-automatic-pneumonia","slug":"deep-learning-for-automatic-pneumonia","title":"Deep Learning for Automatic Pneumonia Detection","date":"2020-05-28","arxiv_id":"2005.13899","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deep-learning-for-automatic-pneumonia#ran","syntology_url":"https://syntology.ai/paper/2005.13899","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.13899"}},"official":{"repos":["tatigabru/kaggle-rsna"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/machine-learning-based-unbalance-detection-of","slug":"machine-learning-based-unbalance-detection-of","title":"Machine Learning-Based Unbalance Detection of a Rotating Shaft Using Vibration Data","date":"2020-05-26","arxiv_id":"2005.12742","repositories_listed":1,"syntology":null},{"url":"/paper/an-interpretable-automated-detection-system","slug":"an-interpretable-automated-detection-system","title":"An interpretable automated detection system for FISH-based HER2 oncogene amplification testing in histo-pathological routine images of breast and gastric cancer diagnostics","date":"2020-05-25","arxiv_id":"2005.12066","repositories_listed":1,"syntology":null},{"url":"/paper/supervised-convex-clustering","slug":"supervised-convex-clustering","title":"Supervised Convex Clustering","date":"2020-05-25","arxiv_id":"2005.12198","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-the-gap-between-natural-and-medical","slug":"bridging-the-gap-between-natural-and-medical","title":"Bridging the gap between Natural and Medical Images through Deep Colorization","date":"2020-05-21","arxiv_id":"2005.10589","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-based-diagnosis-of-covid-19","slug":"deep-learning-based-diagnosis-of-covid-19","title":"Deep Learning based Diagnosis of COVID-19 usingChest CT-scan Images","date":"2020-05-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ultrasound-video-summarization-using-deep","slug":"ultrasound-video-summarization-using-deep","title":"Ultrasound Video Summarization using Deep Reinforcement Learning","date":"2020-05-19","arxiv_id":"2005.09531","repositories_listed":1,"syntology":null},{"url":"/paper/a-tale-of-two-perplexities-sensitivity-of","slug":"a-tale-of-two-perplexities-sensitivity-of","title":"A Tale of Two Perplexities: Sensitivity of Neural Language Models to Lexical Retrieval Deficits in Dementia of the Alzheimer's Type","date":"2020-05-07","arxiv_id":"2005.03593","repositories_listed":1,"syntology":null},{"url":"/paper/petra-a-sparsely-supervised-memory-model-for","slug":"petra-a-sparsely-supervised-memory-model-for","title":"PeTra: A Sparsely Supervised Memory Model for People Tracking","date":"2020-05-06","arxiv_id":"2005.02990","repositories_listed":1,"syntology":null},{"url":"/paper/covid-da-deep-domain-adaptation-from-typical","slug":"covid-da-deep-domain-adaptation-from-typical","title":"COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19","date":"2020-04-30","arxiv_id":"2005.01577","repositories_listed":1,"syntology":null},{"url":"/paper/automated-detection-of-covid-19-cases-using","slug":"automated-detection-of-covid-19-cases-using","title":"Automated detection of COVID-19 cases using deep neural networks with X-ray images","date":"2020-04-28","arxiv_id":null,"repositories_listed":1,"syntology":null}],"record_sha256":"e0407237b0ff4336912f48b3d247919681581a100d4bee59172229b6eee59f1f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}