mlsanityData to 14 Oct 2026

Non-Contrast CT Esophageal Varices Grading through Clinical Prior-Enhanced Multi-Organ Analysis

arXiv 2512.19415 ↗First posted 22 Dec 2025cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Esophageal varices (EV) represent a critical complication of portal hypertension, affecting approximately 60% of cirrhosis patients with a significant bleeding risk of ~30%. While traditionally diagnosed through invasive endoscopy, non-contrast computed tomography (NCCT) presents a potential non-invasive alternative that has yet to be fully utilized in clinical practice. We present Multi-Organ-COhesion Network++ (MOON++), a novel multimodal framework that enhances EV assessment through comprehensive analysis of NCCT scans. Inspired by clinical evidence correlating organ volumetric relationships with liver disease severity, MOON++ synthesizes imaging characteristics of the esophagus, liver, and spleen through multimodal learning. We evaluated our approach using 1,631 patients, those with endoscopically confirmed EV were classified into four severity grades. Validation in 239 patient cases and independent testing in 289 cases demonstrate superior performance compared to conventional single organ methods, achieving an AUC of 0.894 versus 0.803 for the severe grade EV classification (G3 versus <G3) and 0.921 versus 0.793 for the differentiation of moderate to severe grades (>=G2 versus <G2). We conducted a reader study involving experienced radiologists to further validate the performance of MOON++. To our knowledge, MOON++ represents the first comprehensive multi-organ NCCT analysis framework incorporating clinical knowledge priors for EV assessment, potentially offering a promising non-invasive diagnostic alternative.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 56 references that are in the map. First posted on arXiv 22 Dec 2025.

Builds on

22 of 56 references.

References · 22

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  4. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  5. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  6. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  7. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  8. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  9. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,333
  10. 2017The Kinetics Human Action Video Datasetcited by 1,909
  11. 2014Two-Stream Convolutional Networks for Action Recognition in Videoscited by 1,068
  12. 2022TotalSegmentator: robust segmentation of 104 anatomical structures in CT imagescited by 613
10 more
  1. 2016Convolutional Two-Stream Network Fusion for Video Action Recognitioncited by 384
  2. 2018Efficient Low-rank Multimodal Fusion with Modality-Specific Factorscited by 270
  3. 2022UniFormer: Unifying Convolution and Self-attention for Visual Recognitioncited by 206
  4. 2023A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Stagingcited by 13
  5. 2024Robustly Optimized Deep Feature Decoupling Network for Fatty Liver Diseases Detectioncited by 11
  6. 2018Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networkscited by 10
  7. 2023SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformationcited by 9
  8. 2023Video Pretraining Advances 3D Deep Learning on Chest CT Taskscited by 4
  9. 2024Improved Esophageal Varices Assessment from Non-Contrast CT Scanscited by 3
  10. 2025PLUS: Plug-and-Play Enhanced Liver Lesion Diagnosis Model on Non-Contrast CT Scanscited by 1

Built on by

Not cited yet.