Computer-aided diagnosis (CADx) for gastrointestinal (GI) endoscopy increasingly depends on deep models trained end-to-end on raw images. However, raw images are often unavailable in legacy clinical systems or privacy-sensitive settings. This work presents a methodological study on multi-class GI disease classification...
Underwater semantic segmentation is essential for marine ecosystem monitoring, yet remains challenging due to severe visual degradation. Light absorption and scattering often lead to color shifts, low contrast, and blurred boundaries, making shallow detail features unreliable. Existing underwater segmentation methods i...
BAR-UNet improves boundary precision and appearance robustness with modest computational overhead on an NVIDIA RTX 4060 GPU and is a promising research prototype for computer-aided polyp analysis.
This work instantiates CHAINLSTM, a lightweight dual-task LSTM supporting per-event online detection and shows that chain-aware encoding shifts median prediction probability on path anomalies from 0.91 to 0.002, suggesting a wider separation margin for threshold-based detection.
Yi-Liu Xu, Ziwei Hong, Zhongheng Yang et al.· arXiv.org· 4 citations
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