Latest AI and machine learning research in gastroenterology for healthcare professionals.
Minimally invasive and robot-assisted surgery relies heavily on endoscopic imaging, yet surgical smoke produced by electrocautery and vessel-sealing instruments can severely degrade visual perception and hinder vision-based functionalities. We present a transformer-based surgical desmoking model with a physics-inspired desmoking head that jointly predicts smoke-free image and corresponding smoke m...
Early screening via colonoscopy is critical for colon cancer prevention, yet developing robust AI systems for this domain is hindered by the lack of densely annotated, long-sequence video datasets. Existing datasets predominantly focus on single-class polyp detection and lack the rich spatial, temporal, and linguistic annotations required to evaluate modern Multimodal Large Language Models (MLLMs)...
Deep learning and generative models are advancing rapidly, with synthetic data increasingly being integrated into training pipelines for downstream an...
Gastrointestinal (GI) tract image analysis plays a crucial role in medical diagnosis. This research addresses the challenge of accurately classifying ...
Cancer data standardization requires converting unstructured pathology reports into structured registry variables, a mostly manual and resource-intens...
In endoscopic surgery, surgeons continuously locate the endoscopic view relative to the anatomy by interpreting the evolving visual appearance of the ...
Contrast-enhanced magnetic resonance imaging (CE-MRI) plays a crucial role in brain tumor assessment; however, its acquisition requires gadolinium-bas...
Recent vision-language models (VLMs) have shown strong generalization and multimodal reasoning abilities in natural domains. However, their applicatio...
Goal-oriented semantic communication has recently emerged in wireless sensor-actuator networks, emphasizing the meaning and relevance of information o...
In digital pathology, whole-slide images routinely exceed gigapixel resolution, making computationally intensive generative super-resolution (SR) impr...
Colorectal cancer, inflammatory bowel disease, and diverticular disease are progressive conditions that affect millions of individuals worldwide and i...
Computational pathology has made significant progress in recent years, fueling advances in both fundamental disease understanding and clinically ready...
We developed a multi-label gastrointestinal video analysis pipeline based on a ResNet-50 frame classifier followed by anatomy-guided temporal event de...
This work presents a multi-label classification framework for video capsule endoscopy (VCE) that addresses the extreme class imbalance inherent in the...
Objectives Using qualitative methods, this study aimed to provide a comparative overview of the similarities and differences in perspectives towards A...
Purpose: While bowel sound auscultation represents a key component of abdominal examination, its utility is limited because bowel sounds (BS) are inte...
Predicting genetic perturbation responses at a single-cell level is central to building models for cell state and disease. However, existing approache...
Accurate monocular depth estimation is critical in colonoscopy for lesion localization and navigation. Foundation models trained on natural images fai...
Ulcerative colitis (UC) is a chronic mucosal inflammatory condition that places patients at increased risk of colorectal cancer. Colonoscopic surveill...
Histopathology remains the gold standard for cancer diagnosis because it provides detailed cellular-level assessment of tissue morphology. However, ma...