UnCapsTSR is an unsupervised transformer-based GAN framework designed to enhance the spatial resolution of low‑resolution wireless capsule endoscopy (WCE) images. It eliminates the need for explicit degradation modeling or paired LR‑HR data by using a Bilateral Total Variation loss to preserve spatial continuity. The authors introduce a new Kvasir Capsule dataset for training, validate generalizability on KID and GIANA datasets, and propose the Endoscopy Quality Metric (EndoQM) as a non‑reference evaluation tool, reporting 40–80% improvement in EndoQM over state‑of‑the‑art unsupervised methods.
By Anjali Sarvaiya, Shubh Kawa, Lalit Agrawal, Jagrit Joshi, Kishor Upla, Kiran Raja
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.
By Francisco Caetano, Tim J. M. Jaspers, Haiko Middeljans, Martijn R. Jong, Rixta A. H. van Eijck van Heslinga, Floor Slooter, Albert J. de Groof, Jacques J. Bergman, Peter H. N. De With, Fons van der Sommen
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.
By Anjali Sarvaiya, Jay Kadel, Kishor Upla, Kiran Raja
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.
arXiv:2602. 06938v2 Announce Type: replace-cross Abstract: The classification performance of deep neural networks relies strongly on access to large, accurately annotated datasets.
By Julia Werner, Julius Oexle, Oliver Bause, Maxime Le Floch, Franz Brinkmann, Hannah Tolle, Jochen Hampe, Oliver Bringmann
arXiv:2604. 19191v2 Announce Type: replace-cross Abstract: Deploying AI-based anomaly detection across diverse clinical imaging settings remains challenging because most existing methods rely on modality-specific architectures, anatomical priors, or extensive retraining, limiting their use as general-purpose screening tools.
By Pritam Kar, Gouri Lakshmi S, Saptarshi Bej