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.
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
EndoFSA is a GAN-based model designed for endoscopic few-shot image generation, addressing the scarcity of pathological samples in wireless capsule endoscopy (WCE) data. It adapts a generator pretrained on abundant normal images to abnormal domains by updating only a small set of rank-constrained modulation parameters while keeping the rest of the weights frozen, thereby preserving anatomical priors and preventing mode collapse. The method incorporates perceptual boundary regularization and cluster-wise diversity control, operates without pixel-level annotations, and demonstrates that synthetic abnormal images can match real images in downstream classification performance.
By Panagiota Gatoula, Grigoris Karypidis, Dimitris K. Iakovidis
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
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.
By Wang Jiangtao, Nur Intan Raihana Ruhaiyem, Fu Panpan, Yang Yu, Huang Yan
arXiv:2610.20509v1 Announce Type: cross
Abstract: Generative super-resolution (SR) can recover sharp detail in computed tomography (CT) images from severely downsampled observations, but the missing...
By Hairong Ni, Jiahe Huang, Minghao Guo, Wojciech Matusik, Rose Yu
arXiv:2602.20773v2 Announce Type: replace
Abstract: Purpose: Developing generalizable medical image segmentation models is challenging because imaging data are distributed across institutions and dif...
By Sachin Dudda Nagaraju, Ashkan Moradi, Bendik Skarre Abrahamsen, Mattijs Elschot
arXiv:2505.05855v4 Announce Type: replace
Abstract: Multi-contrast super-resolution (MCSR) is crucial for enhancing MRI but current deep learning methods are limited. They typically require large, pa...
By Yinzhe Wu, Hongyu Rui, Fanwen Wang, Jiahao Huang, Zhenxuan Zhang, Haosen Zhang, Zi Wang, Guang Yang
The paper introduces MR‑DiffuSR, a 3‑D latent diffusion framework that uses high‑resolution T1w structural priors to guide super‑resolution of thick‑slice FLAIR MRI scans. By applying cross‑modality structural swin attention and a mixed‑scale degradation strategy, the method avoids hallucinations and remains robust across varying slice thicknesses. On ADNI datasets, MR‑DiffuSR outperforms CNN and 2‑D diffusion baselines, achieving high PSNR, SSIM, and low LPIPS, and maintains strong white‑matter hyperintensity segmentation performance even at 7 mm equivalent slice thickness.
By Haoyu Lan, Jiazhen Zhang, John Onofrey, Bino Varghese, Nasim Sheikh-Bahaei, Arthur W. Toga, Jeiran Choupan
Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges.
SRUG is a supervised, fusion‑driven generator network designed for paired MRI sequence synthesis. It eliminates the generator‑discriminator competition by directly learning a deterministic source‑to‑target mapping, using a residual encoder, full‑resolution stem, and a nested multi‑scale decoder to preserve structural fidelity. Experiments on BraTS 2023 and IXI demonstrate competitive reconstruction quality and cross‑dataset transferability, supporting direct supervised generation as a practical approach for medical image translation.
By Xihe Qiu, Yang Dai, Xiaoyu Tan, Sijia Li, Fenghao Sun, Lu Gan, Liang Liu
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.
By Hongchao Shu, Roger D. Soberanis-Mukul, Hao Ding, Morgan Ringel, Mali Shen, Saif Iftekar Sayed, Hedyeh Rafii-Tari, Mathias Unberath