The paper introduces LAMP, a foreground‑guided framework for synthesizing realistic polyp images that preserves the supplied lesion while generating compatible mucosal tissue. LAMP separates the lesion, valid mucosa, and camera exterior using a field‑of‑view mask, employs lesion‑to‑mucosa cross‑attention to extract appearance conditions, and propagates these conditions over legal tissue support via a FOV‑constrained multidirectional Vision Receptance Weighted Key‑Value mechanism. Experiments on five polyp datasets show that LAMP outperforms existing methods in generation quality and consistently improves five downstream segmentation models.
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.
arXiv:2608. 15537v1 Announce Type: cross Abstract: Accurate boundary delineation remains a persistent challenge in dermoscopic image segmentation because of blurred lesion margins, heterogeneous textures, and complex background artifacts.
GazeRefine is a training‑free framework that uses eye‑gaze data as an inference‑time prompt for zero‑shot medical image segmentation. It converts sparse, duration‑weighted fixations into foreground and background priors that initialize semantic prototypes in a frozen DINOv3 feature space, then iteratively refines these prototypes through discrimination, affinity propagation, and anchoring to the gaze guidance. The method achieves strong results on colonoscopy polyp segmentation and competitive performance on prostate MRI, demonstrating that gaze‑guided prototype refinement can enable segmentation without dense expert annotations or model fine‑tuning.
arXiv:2607. 14328v1 Announce Type: cross Abstract: In proton therapy planning, respiratory-gated non-contrast CT (NCCT) is commonly used for lesion segmentation; however, accurate delineation remains challenging due to low lesion-to-background contrast.
CEM‑TUDASR is a lightweight, unsupervised Transformer-based super‑resolution framework designed to enhance low‑resolution images from Wireless Capsule Endoscopy (WCE). It uses a domain‑adaptive degradation network to generate realistic WCE‑like low‑resolution images from high‑resolution conventional endoscopy data, enabling effective unpaired learning. The model incorporates Deep Attention Blocks and a Fusion Attention Block to capture both global context and fine local details, achieving superior performance on WCE datasets and demonstrating cross‑domain adaptability to retinal images, all while keeping the parameter count and computational load low.
CEM‑TUDASR is a lightweight, unsupervised Transformer‑based super‑resolution framework designed for Wireless Capsule Endoscopy (WCE) images. It uses a domain‑adaptive degradation network to synthesize realistic low‑resolution WCE images from high‑resolution conventional endoscopy data, enabling unpaired training. The SR generator incorporates Deep Attention Blocks and a Fusion Attention Block to preserve both global context and fine local structures, achieving superior no‑reference quality metrics and improved restoration of mucosal textures, vascular patterns, and anatomical details while remaining computationally efficient.
Monocular colonoscopic 3D reconstruction is important for surgical robotic colonoscopy, but remains challenging due to weak texture, specular reflections, limited view overlap, and non-rigid tissue mo...
arXiv:2609.23961v1 Announce Type: new Abstract: Monocular colonoscopic 3D reconstruction is important for surgical robotic colonoscopy, but remains challenging due to weak texture, specular reflectio...
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.
InstEditSeg is a generative framework that treats medical segmentation as an instruction-driven image editing task. Instead of producing binary masks, it renders a color-coded overlay on the original image guided by textual instructions, leveraging latent diffusion models to align with natural image distributions and reduce domain gaps. The method incorporates a DINOv3 visual encoder and a multi-scale feature pyramid fused into the diffusion U‑Net, and uses a dual‑branch classifier‑free guidance strategy to lower inference cost, achieving competitive accuracy on polyp and skin lesion datasets while improving cross‑domain generalization and multi‑lesion segmentation.
arXiv:2505. 17338v3 Announce Type: replace-cross Abstract: Photorealistic volumetric rendering of CT scans greatly benefits clinical workflows, yet neural approaches such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) require prohibitive per-scan optimization (hours for NeRF, about 30 minutes for 3DGS), making them impractical in clinical settings.
arXiv:2609.24116v1 Announce Type: new Abstract: Although deep learning has advanced Whole Slide Image (WSI) Analysis, tissue artifacts like bubbles and folds often cause silent failures by concealing...