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20242026
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eess.IV2025

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology

Can Cui, Xindong Zheng, Ruining Deng +8

Anomaly detection has been widely studied in the context of industrial defect inspection, with numerous methods developed to tackle a range of challenges. In digital pathology, ano…

eess.IV2025

IRS: Incremental Relationship-guided Segmentation for Digital Pathology

Ruining Deng, Junchao Zhu, Juming Xiong +14

Continual learning is rapidly emerging as a key focus in computer vision, aiming to develop AI systems capable of continuous improvement, thereby enhancing their value and practica…

eess.IV2025

Cross-Species Data Integration for Enhanced Layer Segmentation in Kidney Pathology

Junchao Zhu, Mengmeng Yin, Ruining Deng +6

Accurate delineation of the boundaries between the renal cortex and medulla is crucial for subsequent functional structural analysis and disease diagnosis. Training high-quality de…

eess.IV2025

Dataset Distillation in Medical Imaging: A Feasibility Study

Muyang Li, Can Cui, Quan Liu +4

Data sharing in the medical image analysis field has potential yet remains underappreciated. The aim is often to share datasets efficiently with other sites to train models effecti…

eess.IV2025

Assessment of Cell Nuclei AI Foundation Models in Kidney Pathology

Junlin Guo, Siqi Lu, Can Cui +14

Cell nuclei instance segmentation is a crucial task in digital kidney pathology. Traditional automatic segmentation methods often lack generalizability when applied to unseen datas…

eess.IV2025

GLAM: Glomeruli Segmentation for Human Pathological Lesions using Adapted Mouse Model

Lining Yu, Mengmeng Yin, Ruining Deng +9

Moving from animal models to human applications in preclinical research encompasses a broad spectrum of disciplines in medical science. A fundamental element in the development of…