DLiPath: A Benchmark for the Comprehensive Assessment of Donor Liver Based on Histopathological Image Dataset

2025/05/30 by Liangrui Pan, Xingchen Li, Pan, Liangrui +7
Computer Science · Medicine · #AI in cancer detection #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Hepatocellular Carcinoma Treatment and Prognosis #Image and Video Processing (eess.IV) #Organ Transplantation Techniques and Outcomes #Quantitative Methods (q-bio.QM) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2506.03185

openalex publication_date 2025/05/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Pathologists comprehensive evaluation of donor liver biopsies provides crucial information for accepting or discarding potential grafts. However, rapidly and accurately obtaining these assessments intraoperatively poses a significant challenge for pathologists. Features in donor liver biopsies, such as portal tract fibrosis, total steatosis, macrovesicular steatosis, and hepatocellular ballooning are correlated with transplant outcomes, yet quantifying these indicators suffers from substantial inter- and intra-observer variability. To address this, we introduce DLiPath, the first benchmark for comprehensive donor liver assessment based on a histopathology image dataset. We collected and publicly released 636 whole slide images from 304 donor liver patients at the Department of Pathology, the Third Xiangya Hospital, with expert annotations for key pathological features (including cholestasis, portal tract fibrosis, portal inflammation, total steatosis, macrovesicular steatosis, and hepatocellular ballooning). We selected nine state-of-the-art multiple-instance learning (MIL) models based on the DLiPath dataset as baselines for extensive comparative analysis. The experimental results demonstrate that several MIL models achieve high accuracy across donor liver assessment indicators on DLiPath, charting a clear course for future automated and intelligent donor liver assessment research. Data and code are available at https://github.com/panliangrui/ACM_MM₂025.

Citations

• Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer
• DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification
• Attention-Challenging Multiple Instance Learning for Whole Slide Image Classification
• Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images
• Transformer based multiple instance learning for weakly supervised histopathology image segmentation
• Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction
• DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification
• TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification
• Artificial intelligence and computational pathology
• Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning
• Data Efficient and Weakly Supervised Computational Pathology on Whole\n Slide Images
• Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification
• Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
• Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

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• Liver Fibrosis Quantification and Analysis: The LiQA Dataset and Baseline Method