{"url":"/task/retinal-oct-disease-classification","name":"Retinal OCT Disease Classification","slug":"retinal-oct-disease-classification","description_markdown":"Classifying different Retinal degeneration from Optical Coherence Tomography Images (OCT).","categories":[{"name":"Computer Vision","url":"/area/computer-vision"},{"name":"Medical","url":"/area/medical"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":12,"papers_with_code":10,"benchmarks":2,"benchmark_tables_in_archive":2,"benchmark_tables_shown":2,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":2,"subtasks":0,"parent_tasks":2},"benchmarks":[{"leaderboard":"/sota/retinal-oct-disease-classification-on-oct2017","slug":"retinal-oct-disease-classification-on-oct2017","dataset":"OCT2017","dataset_url":null,"rows_in_archive":16,"metrics":["Acc","Sensitivity"],"first_row_in_archive_order":{"model":"UniNet","paper_title":"UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection","paper_url":"/paper/uninet-a-contrastive-learning-guided-unified","paper_date":"2025-02-28","arxiv_id":null,"code_links":[{"title":"pangdatangtt/UniNet","url":"https://github.com/pangdatangtt/UniNet"}],"syntology":null}},{"leaderboard":"/sota/retinal-oct-disease-classification-on","slug":"retinal-oct-disease-classification-on","dataset":"Srinivasan2014","dataset_url":null,"rows_in_archive":14,"metrics":["Acc"],"first_row_in_archive_order":{"model":"OpticNet-71","paper_title":"Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images","paper_url":"/paper/optic-net-a-novel-convolutional-neural","paper_date":"2019-10-13","arxiv_id":"1910.05672","code_links":[{"title":"SharifAmit/OCT_Classification","url":"https://github.com/SharifAmit/OCT_Classification"},{"title":"SharifAmit/OpticNet-71","url":"https://github.com/SharifAmit/OpticNet-71"}],"syntology":null}}],"datasets":[{"url":"/dataset/octid","name":"OCTID","full_name":"Optical Coherence Tomography Image Retinal Database","num_papers_in_archive":15},{"url":"/dataset/retouch","name":"RETOUCH","full_name":"RETOUCH -The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge","num_papers_in_archive":2}],"subtasks":[],"parent_tasks":[{"url":"/task/disease-prediction","name":"Disease Prediction"},{"url":"/task/medical-diagnosis","name":"Medical Diagnosis"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":10,"of":10,"tagged_in_all":12,"items":[{"url":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","arxiv_id":"1512.03385","repositories_listed":484,"syntology":{"n":377,"n_ran":230,"n_unverified":147,"n_pointer_only":187}},{"url":"/paper/mobilenetv2-inverted-residuals-and-linear","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","date":"2018-01-13","arxiv_id":"1801.04381","repositories_listed":159,"syntology":{"n":111,"n_ran":85,"n_unverified":26,"n_pointer_only":64}},{"url":"/paper/rethinking-the-inception-architecture-for","title":"Rethinking the Inception Architecture for Computer Vision","date":"2015-12-02","arxiv_id":"1512.00567","repositories_listed":113,"syntology":{"n":26,"n_ran":5,"n_unverified":21,"n_pointer_only":4}},{"url":"/paper/optic-net-a-novel-convolutional-neural","title":"Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images","date":"2019-10-13","arxiv_id":"1910.05672","repositories_listed":2,"syntology":null},{"url":"/paper/uninet-a-contrastive-learning-guided-unified","title":"UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection","date":"2025-02-28","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/vit-2spn-vision-transformer-based-dual-stream","title":"ViT-2SPN: Vision Transformer-based Dual-Stream Self-Supervised Pretraining Networks for Retinal OCT Classification","date":"2025-01-28","arxiv_id":"2501.17260","repositories_listed":1,"syntology":null},{"url":"/paper/tinc-temporally-informed-non-contrastive","title":"TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes","date":"2022-06-30","arxiv_id":"2206.15282","repositories_listed":1,"syntology":null},{"url":"/paper/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/improving-robustness-using-joint-attention","title":"Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images","date":"2020-05-16","arxiv_id":"2005.08094","repositories_listed":1,"syntology":null},{"url":"/paper/retinal-oct-disease-classification-with","title":"Retinal OCT disease classification with variational autoencoder regularization","date":"2019-03-23","arxiv_id":"1904.00790","repositories_listed":1,"syntology":null}],"syntology_records":3,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}