bibtext: @InProceedings{ Mob_Harnessing_MICCAI2024,
                   author = { Mobadersany, Pooya and Parmar, Chaitanya and Damasceno, Pablo F. and Fadnavis, Shreyas and Chaitanya, Krishna and Li, Shilong and Schwab, Evan and Xiao, Jaclyn and Surace, Lindsey and Mansi, Tommaso and Cula, Gabriela Oana and Ghanem, Louis R. and Standish, Kristopher },
                   title = { { Harnessing Temporal Information for Precise Frame-Level Predictions in Endoscopy Videos } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15006 },
                   month = {October},
                   pages = { pending },
                }
abstract: Camera localization in endoscopy videos plays a fundamental role in enabling precise diagnosis and effective treatment planning for patients with Inflammatory Bowel Disease (IBD). Precise frame-level classification, however, depends on long-range temporal dynamics, ranging from hundreds to tens of thousands of frames per video, challenging current neural network approaches. To address this, we propose EndoFormer, a frame-level classification model that leverages long-range temporal information for anatomic segment classification in gastrointestinal endoscopy videos. EndoFormer combines a Foundation Model block, judicious video-level augmentations, and a Transformer classifier for frame-level classification while maintaining a small memory footprint. Experiments on 4160 endoscopy videos from four clinical trials and over 61 million frames demonstrate that EndoFormer has an AUC=0.929, significantly improving state-of-the-art models for anatomic segment classification. These results highlight the potential for adopting EndoFormer in endoscopy video analysis applications that require long-range temporal dynamics for precise frame-level predictions.
title: Harnessing Temporal Information for Precise Frame-Level Predictions in Endoscopy Videos
authors: Mobadersany, Pooya Parmar, Chaitanya Damasceno, Pablo F. Fadnavis, Shreyas Chaitanya, Krishna Li, Shilong Schwab, Evan Xiao, Jaclyn Surace, Lindsey Mansi, Tommaso Cula, Gabriela Oana Ghanem, Louis R. Standish, Kristopher