arXiv:2511. 01143v2 Announce Type: replace-cross Abstract: Early and accurate segmentation of colorectal polyps is critical for reducing colorectal cancer mortality, which has been extensively explored by academia and industry.
SegCol is a new dataset and benchmark for semantic segmentation of colon fold edges and surgical instruments in colonoscopy images, derived from the EndoMapper dataset. It offers manually annotated pixel‑level masks for three instrument classes and thin fold‑edge structures across temporally consistent image sequences, and serves as the basis for the SegCol Challenge within the EndoVis Challenge at MICCAI 2024. The study evaluates supervised segmentation and annotation‑efficient active learning, analyzes various segmentation metrics under structural perturbations, and highlights how metric behavior depends on target structure, underscoring the need for carefully selected evaluation protocols in endoscopic segmentation.
The paper presents an automated segmentation pipeline for whole‑slide histopathology images of colorectal cancer, labeling tumor grades 1‑3 and normal mucosa. It employs dense prediction transformers with multiple encoder backbones, overlapping patches, test‑time augmentation, and an adaptive augmentation policy guided by large language models. The approach, combined with soft‑voting ensembles and post‑processing refinements, raises the F1 score from 62.92 to 69.84 on a colorectal cancer grade dataset.
Colorectal cancer (CRC) is the second most deadly and third most common cancer, and the leading cause of death among gastrointestinal cancers. Early diagnosis is crucial for the treatment of this canc...
The paper introduces GPF‑Net, a Gated Progressive Fusion Network designed for colonoscopic polyp re‑identification. It fuses multimodal features from multiple levels using fully connected gating mechanisms, allowing layer‑wise refinement of semantic information. Experiments on standard benchmarks show that this multimodal, progressive fusion approach outperforms state‑of‑the‑art unimodal ReID models, especially for small polyps where fine‑grained details are crucial.
arXiv:2608.22281v1 Announce Type: cross Abstract: Medical image segmentation requires high accuracy and robustness, yet practical commercial deployment also demands privacy preservation and computati...
arXiv:2608. 07176v1 Announce Type: cross Abstract: Developing foundation generative models for endoscopy is limited by the gap between natural and clinical images and the computational cost of training large Diffusion Transformers.
arXiv:2608. 15580v1 Announce Type: new Abstract: Reliable endoscopic polyp reporting requires integrating quantitative lesion sizing, standardized Paris classification, and clinically meaningful morphological description within a single record.
arXiv:2607.12896v3 Announce Type: replace Abstract: Medical image segmentation foundation models are expected to generalize across diverse clinical scenarios, yet existing universal methods remain fr...
arXiv:2606. 17340v1 Announce Type: cross Abstract: Accurate vision-based navigation in monocular endoscopy is difficult due to limited depth cues, weak tissue texture, non-rigid deformation, and substantial appearance variation across domains, all of which complicate pose estimation, depth prediction, and image-to-anatomy alignment.
The paper introduces FreNet, a feature reconfiguration framework that incorporates visual priors for medical lesion segmentation. FreNet performs pixel‑level reconfiguration before encoding using an Implicit Prior Neural Network (IPNN) that leverages SAM, and feature‑level reconfiguration during encoding via a Dual‑domain Feature Reconfiguration (DFR) module, which includes a Frequency Decoupling Module (FDM) and a Spatial Localization Module (SLM). Experiments on nine benchmarks across three imaging modalities show that FreNet outperforms state‑of‑the‑art methods, achieving a 5.0% Dice improvement over the best baseline on the ETIS dataset and a 7.2% improvement over SAM.
arXiv:2610.29090v1 Announce Type: new Abstract: Abdominal organ segmentation is essential in modern medical applications. While existing deep learning approaches demonstrate promising performance, th...