Published SOTA gains in colonoscopy polyp segmentation are largely non-comparable because of omitted clinical metrics, incompatible splits, and missing significance tests, as shown by a 27-paper audit and uniform re-evaluation.
Machine Intelligence Research 19(6), 531–549 (2022)
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A Temporally Consistent Learning Framework distills temporal coherence into 2D networks for real-time TRUS prostate video segmentation via confidence-weighted consistency, dual-scale prototype alignment, and geometric pseudo-labeling.
OBBSeg segments irregular medical lesions from oriented bounding-box labels via a Mask-to-OBB loss and prompt modules, claiming near fully-supervised accuracy across 13 datasets and 5 modalities.
citing papers explorer
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Metrics or Mirage? An Audit of Evaluation Inconsistencies in Colonoscopy Polyp Segmentation Benchmarks
Published SOTA gains in colonoscopy polyp segmentation are largely non-comparable because of omitted clinical metrics, incompatible splits, and missing significance tests, as shown by a 27-paper audit and uniform re-evaluation.
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Distilling Temporal Coherence into 2D Networks for Transrectal Ultrasound Prostate Video Segmentation
A Temporally Consistent Learning Framework distills temporal coherence into 2D networks for real-time TRUS prostate video segmentation via confidence-weighted consistency, dual-scale prototype alignment, and geometric pseudo-labeling.
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OBBSeg: Irregular Lesion Segmentation under Oriented Bounding Box Annotations
OBBSeg segments irregular medical lesions from oriented bounding-box labels via a Mask-to-OBB loss and prompt modules, claiming near fully-supervised accuracy across 13 datasets and 5 modalities.