{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/paper/jointly-learning-convolutional","title":"Jointly Learning Convolutional Representations to Compress Radiological Images and Classify Thoracic Diseases in the Compressed Domain","arxiv_id":null,"date":"2018-12-18","proceeding":"ICVGIP 2018 2018 12","authors":["Ekagra Ranjan","Soumava Paul","Siddharth Kapoor","Aupendu Kar","Ramanathan Sethuraman","Debdoot Sheet"],"abstract":"Deep learning models trained in natural images are commonly used for different classification tasks in the medical domain. Generally, very high dimensional medical images are down-sampled by us- ing interpolation techniques before feeding them to deep learning models that are ImageNet compliant and accept only low-resolution images of size 224 × 224 px. This popular technique may lead to the loss of key information thus hampering the classification. Signifi- cant pathological features in medical images typically being small sized and highly affected. To combat this problem, we introduce a convolutional neural network (CNN) based classification approach which learns to reduce the resolution of the image using an autoen- coder and at the same time classify it using another network, while both the tasks are trained jointly. This algorithm guides the model to learn essential representations from high-resolution images for classification along with reconstruction. We have used the publicly available dataset of chest x-rays to evaluate this approach and have outperformed state-of-the-art on test data. Besides, we have experi- mented with the effects of different augmentation approaches in this dataset and report baselines using some well known ImageNet class of CNNs.","url_abs":"https://drive.google.com/file/d/1i2jl5M0ddr-STAma0a2Bsr5rOMtcCSyB/view","url_pdf":"https://drive.google.com/file/d/1i2jl5M0ddr-STAma0a2Bsr5rOMtcCSyB/view","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"jointly-learning-convolutional","repo_url":"https://github.com/ekagra-ranjan/AE-CNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"pneumonia-detection","task_name":"Pneumonia Detection"},{"task_slug":"thoracic-disease-classification","task_name":"Thoracic Disease Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/pneumonia-detection-on-chestx-ray14","task":"Pneumonia Detection","dataset":"ChestX-ray14","model":"AE-CNN","rank_in_archive_order":5,"of":5,"metrics":{"AUROC":"0.8241"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}