Gastroenterology

Latest AI and machine learning research in gastroenterology for healthcare professionals.

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Test-Time Modality Generalization for Medical Image Segmentation

Generalizable medical image segmentation is essential for ensuring consistent performance across d...

Towards an AI co-scientist

Scientific discovery relies on scientists generating novel hypotheses that undergo rigorous experi...

Enhancing Hepatopathy Clinical Trial Efficiency: A Secure, Large Language Model-Powered Pre-Screening Pipeline

Background: Recruitment for cohorts involving complex liver diseases, such as hepatocellular carci...

Robust Polyp Detection and Diagnosis through Compositional Prompt-Guided Diffusion Models

Colorectal cancer (CRC) is a significant global health concern, and early detection through screen...

[Pancreas segmentation with multi-channel convolution and combined deep supervision].

Due to its irregular shape and varying contour, pancreas segmentation is a recognized challenge in m...

Feb 2025 40000186
A Reverse Mamba Attention Network for Pathological Liver Segmentation

We present RMA-Mamba, a novel architecture that advances the capabilities of vision state space mo...

Liver Cirrhosis Stage Estimation from MRI with Deep Learning

We present an end-to-end deep learning framework for automated liver cirrhosis stage estimation fr...

Deep learning approaches to surgical video segmentation and object detection: A Scoping Review

Introduction: Computer vision (CV) has had a transformative impact in biomedical fields such as ra...

MedForge: Building Medical Foundation Models Like Open Source Software Development

Foundational models (FMs) have made significant strides in the healthcare domain. Yet the data sil...

Benchmarking machine learning for bowel sound pattern classification from tabular features to pretrained models

The development of electronic stethoscopes and wearable recording sensors opened the door to the a...

TRUSWorthy: Toward Clinically Applicable Deep Learning for Confident Detection of Prostate Cancer in Micro-Ultrasound

While deep learning methods have shown great promise in improving the effectiveness of prostate ca...

MedFuncta: Modality-Agnostic Representations Based on Efficient Neural Fields

Recent research in medical image analysis with deep learning almost exclusively focuses on grid- o...

SHADeS: Self-supervised Monocular Depth Estimation Through Non-Lambertian Image Decomposition

Purpose: Visual 3D scene reconstruction can support colonoscopy navigation. It can help in recogni...

Differentially private fine-tuned NF-Net to predict GI cancer type

Based on global genomic status, the cancer tumor is classified as Microsatellite Instable (MSI) an...

Application of artificial intelligence in the detection of Borrmann type 4 advanced gastric cancer in upper endoscopy (with video).

BACKGROUND: Borrmann type-4 (B-4) advanced gastric cancer is challenging to diagnose through routine...

Feb 2025 39955610
Towards Polyp Counting In Full-Procedure Colonoscopy Videos

Automated colonoscopy reporting holds great potential for enhancing quality control and improving ...

Leveraging Machine Learning and Deep Learning Techniques for Improved Pathological Staging of Prostate Cancer

Prostate cancer (Pca) continues to be a leading cause of cancer-related mortality in men, and the ...

Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation

Employing self-supervised learning (SSL) methodologies assumes par-amount significance in handling...

TEMSET-24K: Densely Annotated Dataset for Indexing Multipart Endoscopic Videos using Surgical Timeline Segmentation

Indexing endoscopic surgical videos is vital in surgical data science, forming the basis for syste...

Diverse Image Generation with Diffusion Models and Cross Class Label Learning for Polyp Classification

Pathologic diagnosis is a critical phase in deciding the optimal treatment procedure for dealing w...

Self-Supervised Learning for Pre-training Capsule Networks: Overcoming Medical Imaging Dataset Challenges

Deep learning techniques are increasingly being adopted in diagnostic medical imaging. However, th...

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