Colorectal Cancer Screening and Detection

Latest papers

• SCOPE-4D: Endoscopic 4D Geometry Foundation Models

  Chaoyi Zhou, Zhongpai Gao, Anwesa Choudhuri, Meng Zheng, Benjamin Planche, Run Wang, Terrence Chen, Siyu Huang, Ziyan Wu · 5 October 2026

  Geometric understanding supports endoscopic navigation and robotic assistance, but learning reliable endoscopic geometry faces two challenges: scarce geometric annotations and ambiguity between camera motion and tissue deformation. We present SCOPE-4D, an endoscopic 4D geometry foundation model that…

• Preserving Anatomical Continuity: Three-Stage Pipeline for Colon Segmentation in 3D Abdominal CT Scans

  Deshan Kalupahana, Sonit Singh, Praveen Ravindran, Arcot Sowmya · 5 October 2026

  Accurate colon segmentation from CT images is essential for colorectal disease analysis, yet deep learning based methods often produce disconnected predictions due to complex anatomy. This study introduces a three-stage, topology-preserving segmentation pipeline to address this issue. The first stag…

• EndoFSA: Endoscopic Few-Shot Image Generation via Rank-Constrained Parameter Adaptation

  Panagiota Gatoula, Grigoris Karypidis, Dimitris K. Iakovidis · 25 September 2026

  WCE produces large-scale gastrointestinal image data yet pathological findings remain significantly underrepresented limiting the generalization performance of deep-learning based abnormality detection systems. SDG methods offer a practical solution to mitigate this imbalance. However their training…

• Colon3R: Cross-Domain 3D Reconstruction from Monocular Colonoscopic Video

  Zhihao Xing, Yingyu Wang, Liang Zhao, Shoudong Huang · 22 September 2026

  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 motion. Conventional multi-view 3D reconstruction methods rely on stable correspondences and approxima…

• Beyond the Foreground: FOV-Aware Polyp Image Synthesis via Lesion-Guided Adaptive Mucosal Context Propagation

  Tong Wang, Yuting He, Bin Ren, Yutong Xie, Guanyu Yang · 18 September 2026

  Synthetic image and mask pairs can alleviate scarce colonoscopy annotations, but realistic synthesis requires preserving the supplied lesion while generating compatible mucosa. Existing foreground-guided methods treat all non-foreground pixels as background and rely mainly on local integration. Dire…

• ERCPMP-Gx: Endoscopic Image and Video Dataset for Morphological, Histopathological, and Genomic Characterization of Colorectal Polyposis

  Zahra Ghaffari, Massih Bahar, Mojgan Forootan, Ali Darvishi, Hamidreza Bolhasani · 18 September 2026

  Hereditary polyposis syndromes can be precursor lesions to colorectal cancer and are associated with a broad spectrum of extracolonic tumors. Early identification and accurate classification of these syndromes are essential for timely diagnosis, individualized patient management, and targeted survei…

• Lesion-centered 3D mapping of colonoscopy procedures: validation of a hierarchical ensemble pipeline on public benchmark videos

  Hyunjun Kim, Hyeonwoo Na, Jaewoo Lee · 16 September 2026

  Background and Objective: Colonoscopy recording practice preserves text reports and still photographs, while the spatial information already present in the recorded video - where the scope traveled, where a lesion was observed, and whether the same lesion was seen again - is discarded when the proce…

• SegCol Challenge: Semantic Segmentation for Tools and Fold Edges in Colonoscopy data

  Xinwei Ju, Rema Daher, Razvan Caramalau, Baoru Huang, Danail Stoyanov, Francisco Vasconcelos · 11 September 2026

  Improving the reliability and completeness of colonoscopic inspection is critical for reducing missed lesions and improving colorectal cancer prevention. Reliable scene understanding is essential for navigation, reconstruction, and assessment of inspection completeness. Anatomical structures such as…

• Cross-Model Agreement as a Deployment-Time Reliability Signal for Automatic Polyp Segmentation

  Siddharth Gupta, Jitin Singla · 10 September 2026

  In real-time colonoscopy, ground-truth annotations are unavailable at inference, so polyp segmentation models can fail silently. We propose Referee-Based Quality Estimation (RBQE), a reference-free framework measuring agreement between a primary segmentation model and an independently trained refere…

• WSPolypNet: Weakly Supervised Polyp Localization in Colonoscopy Videos

  Giseong Hwang, Minjae Jo, Yeonghyeon Park, Kyeonghun Kim, Seoyeon Han, Donghoon Han, Haneul Kim, Yului Jeong, Insung Hwang, Pa Hong, Ken Ying-Kai Liao, Nam-Joon Kim · 9 September 2026

  Because dense frame-level annotation of colonoscopy videos is costly, we propose WSPolypNet, a weakly supervised framework for polyp localization using only video-level labels. WSPolypNet employs a 3D convolutional neural network trained with video-level supervision to generate class activation maps…

• MultiAttenGastro: Multi-Dimensional Attention Augmentation for Gastrointestinal Endoscopy Classification

  Sadhana Devarajan, Praveen Kumar Chandaliya, Dhruvin Jashvant Kumar Shah, Kishor Upla, Kiran Raja · 7 September 2026

  Automated gastrointestinal (GI) endoscopy classification requires models that generalize across diverse modalities and class distributions, often far from natural-image pretraining. We propose MultiAttenGastro, a plug-and-play attention framework with parallel 1-D channel, 2-D spatial, and 3-D conte…

• Endoscopic Depth Estimation Based on Deep Learning: A Survey

  Ke Niu, Zeyun Liu, Xue Feng, Heng Li, Naian Xiao, Binghua Su, Qika Lin, Kaize Shi · 2 September 2026

  Endoscopic depth estimation is a critical technology for improving the safety and precision of minimally invasive surgery. It has attracted considerable attention from researchers in medical imaging, computer vision, and robotics. Over the past decade, a large number of methods have been developed. …

• From Generalist to Specialist: A Context-Fusion Framework for Endoscopic Polyp Reporting with a Frozen VLM

  Ruijie Yang, Yan Zhu, Peiyao Fu, Siyuan Li, Te Luo, Zhihua Wang, Quanlin Li, Pinghong Zhou, Xian Yang, Shuo Wang · 18 August 2026

  Reliable endoscopic polyp reporting requires integrating quantitative lesion sizing, standardized Paris classification, and clinically meaningful morphological description within a single record. General-purpose vision-language models (VLMs) offer a unified interface for image understanding and repo…

• CSG-Mamba: A Convolutional Scoring Gating Vision State Space Network for Endoscopic Polyp Segmentation

  Yuliang Wang, Jiaqi Wu, Jiaye Song, Shuxia Ren · 17 August 2026

  Accurate polyp segmentation is critical for computer-aided colonoscopy, yet endoscopic images often contain low-contrast boundaries, mucosal texture interference, specular highlights, and device-dependent appearance shifts. Vision State Space Models (SSMs) provide efficient long-range modeling with …

• TRUE-Colon: Exposing a Consistent Transfer Asymmetry in Real-Time Polyp Detection

  Sebastian Doerrich, Andreas Franz Schwab, Francesco Di Salvo, Shyam Nandan Rai, Hanh Huyen My Nguyen, Christian Ledig · 17 August 2026

  Computer-aided detection (CADe) systems for colonoscopy promise to reduce clinical miss rates, yet reliable real-world deployment remains elusive. This translational gap stems in part from a structural flaw in model development: the reliance on curated datasets that under-represent the long negative…

• MicroAUNet: Boundary-Enhanced Multi-scale Fusion with Knowledge Distillation for Colonoscopy Polyp Image Segmentation

  Ziyi Wang, Yuanmei Zhang, Baoying Ye, Yimei Jiang, Leilei Gu, Suncheng Xiang · 13 August 2026

  Early and accurate segmentation of colorectal polyps is critical for reducing colorectal cancer mortality, which has been extensively explored by academia and industry. However, current deep learning-based polyp segmentation models either compromise clinical decision-making by providing ambiguous po…

• PolypVision: A Three-Stage Hierarchical Deep Learning Framework for Classification and Segmentation of Colorectal Polyps

  Hamidreza Bolhasani, Hamidreza Rastad, Amir Mohammad Akbari, Mohammad Tashakoripour, Parnian Asadollahi, Ata Khodami, Mojgan Forootan · 12 August 2026

  Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, predominantly arising from precancerous polyps. Accurate detection, segmentation, and endoscopic and histological classification of colorectal polyps are crucial for timely clinical intervention. In this…

• Performance of large language models in the optical diagnosis of colorectal polyps

  Joshua C. Vences, William T. Tran, Nikko Gimpaya, Catharine M. Walsh, Rishad J. Khan, Robert Bechara, Asher C. Wiggins, Celine N. Rousan, Kaitlyn V. G. L. Morgado, Angie Ibrahim, Kevin H. M. Kuo, Daniel von Renteln, Alexander Hann, Dennis L. Shung, Michael A. Scaffidi, Charles M\'enard, Joshua Landy, Samir C. Grover · 11 August 2026

  Background and Study Aims: Accurate optical diagnosis of colorectal polyps guides resection strategy and surveillance, with multimodal large language models (MLLMs) showing potential for image-based diagnosis. We aimed to evaluate the diagnostic accuracy of MLLMs in classifying colorectal polyps and…

• Representation-driven Endoscopic Visual Embedding Alignment for Latent Generation

  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 · 10 August 2026

  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. Although representation alignment has improved efficiency in general computer vision, its role within the highly specia…

• Boosting Generalizable Depth Estimation in Endoscopy by Mixture of Lightweight Experts and Intrinsic Image Alignment

  Liangjing Shao, Beilei Cui, Yiming Huang, Changjing Liu, Hongliang Ren · 4 August 2026

  Depth estimation is a significant task for 3D perception in endoscopic surgeries. However, illumination interference and feature diversity in various endoscopic scenes are still challenges for generalizable depth estimation and ego-motion estimation. Based on this, a novel self-supervised framework,…

• QFedPolyp: A Communication- and Inference-Efficient Federated Learning Framework for Polyp Segmentation

  Madan Baduwal, Priyanka Paudel · 28 July 2026

  Background and Objective: Automatic polyp segmentation supports computer-aided diagnosis and early colorectal cancer detec- tion. Centralized deep learning requires hospitals to share sensitive medical data, while federated learning preserves privacy but introduces high communication costs through r…

• Induce to Empower: Improving Lightweight Baselines via Foundation Model Induction for Generalized Polyp Segmentation

  Shivanshu Agnihotri, Snehashis Majhi, Deepak Ranjan Nayak, Dwarikanath Mahapatra, Debesh Jha · 21 July 2026

  Automated polyp segmentation in colonoscopy continues to pose challenges due to substantial appearance variations and indistinct polyp boundaries. Although emerging foundation models (FMs) such as DINOv2, SAM, and OneFormer, demonstrate remarkable generalization capabilities, their direct transfer t…

• MAGE: Color-Invariant and Spatial Knowledge Distillation for Gastric Neoplasm Classification

  Jiho Jun, Jeongwon Woo, Jaemin Song, Thanh Bong Nguyen, Dong-heon Yeon, Donghoon Kang, Jae-Myung Park, Sung-Jea Ko, Kwang-Hyun Uhm · 15 July 2026

  Accurate differentiation between gastric adenoma and carcinoma during endoscopy is critical for clinical decision-making. Yet, this task is highly challenging due to high inter-class similarity and ambiguous boundaries between the two classes. Existing ROI-based classification methods often suffer f…

• Metrics or Mirage? An Audit of Evaluation Inconsistencies in Colonoscopy Polyp Segmentation Benchmarks

  Aisha Urooj, Zain Ul Abdien, Neelu Madan · 10 July 2026

  Progress in colonoscopy polyp segmentation is routinely reported through leaderboard comparisons on a small set of public benchmarks. We argue that this apparent progress is difficult to verify: a systematic audit of \textbf{27 papers} published between 2015 and 2026 reveals three structural problem…

• Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs

  Julia Werner, Christoph Gerum, Moritz Reiber, J\"org Nick, Oliver Bringmann · 10 July 2026

  This paper presents a method to efficiently classify the gastroenterologic section of images derived from Video Capsule Endoscopy (VCE) studies by exploring the combination of a Convolutional Neural Network (CNN) for classification with the time-series analysis properties of a Hidden Markov Model (H…