ResNet-50 with Class Reweighting and Anatomy-Guided Temporal Decoding for Gastrointestinal Video Analysis
arXiv:2603.17784v2 Announce Type: replace-cross Abstract: We developed a multi-label gastrointestinal video analysis pipeline based on a ResNet-50 frame classifier followed by anatomy-guided temporal event decoding. The system predicts 17 labels, including 5 anatomy classes and 12 pathology classes, from frames resized to 336x336. A major challenge was severe class imbalance, particularly for rare pathology labels. To address this, we used clipped class-wise positive weighting in the training loss, which improved rare-class learning while maintaining stable optimization. At the temporal stage,
The proliferation of high-resolution video data in medical diagnostics, coupled with advancements in deep learning, makes precision AI solutions for complex analytical tasks like gastrointestinal video analysis increasingly feasible.
This development represents a significant step towards autonomous and accurate medical diagnostics, potentially reducing diagnostic errors and improving patient outcomes through AI-driven insights from complex visual data.
The ability to automatically classify and decode events from gastrointestinal videos using AI-guided temporal analysis shifts diagnostic paradigms from manual, labor-intensive review to automated, precise machine interpretation.
- · Medical AI developers
- · Healthcare providers
- · Medical imaging equipment manufacturers
- · Patients needing GI diagnostics
- · Traditional manual diagnostic services
Physicians will gain faster and more consistent diagnostic reports for gastrointestinal conditions.
The cost and accessibility of advanced gastrointestinal diagnostics could improve, enabling broader screening and earlier disease detection.
This precision in medical video analysis could extend to other complex internal imaging, fostering a new class of specialized diagnostic AI agents across medical fields.
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Read at arXiv cs.LG