8 citations · 17 across the 10 of their papers we have counts for
10 papers
EndoCIL: A Class-Incremental Learning Framework for Endoscopic Image Classification
Bingrong Liu, Jun Shi, Yushan Zheng
Class-incremental learning (CIL) for endoscopic image analysis is crucial for real-world clinical applications, where diagnostic models should continuously adapt to evolving clinic…
Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis
Kunming Tang, Zhiguo Jiang, Jun Shi +3
Gigapixel image analysis, particularly for whole slide images (WSIs), often relies on multiple instance learning (MIL). Under the paradigm of MIL, patch image representations are e…
Slide-based Graph Collaborative Training for Histopathology Whole Slide Image Analysis
Jun Shi, Tong Shu, Zhiguo Jiang +3
The development of computational pathology lies in the consensus that pathological characteristics of tumors are significant guidance for cancer diagnostics. Most existing research…
SlideGCD: Slide-based Graph Collaborative Training with Knowledge Distillation for Whole Slide Image Classification
Tong Shu, Jun Shi, Dongdong Sun +2
Existing WSI analysis methods lie on the consensus that histopathological characteristics of tumors are significant guidance for cancer diagnostics. Particularly, as the evolution…
Lifelong Histopathology Whole Slide Image Retrieval via Distance Consistency Rehearsal
Xinyu Zhu, Zhiguo Jiang, Kun Wu +2
Content-based histopathological image retrieval (CBHIR) has gained attention in recent years, offering the capability to return histopathology images that are content-wise similar…
Pan-cancer Histopathology WSI Pre-training with Position-aware Masked Autoencoder
Kun Wu, Zhiguo Jiang, Kunming Tang +5
Large-scale pre-training models have promoted the development of histopathology image analysis. However, existing self-supervised methods for histopathology images primarily focus…
Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image Classification
Yushan Zheng, Jun Li, Jun Shi +2
Transformer has been widely used in histopathology whole slide image (WSI) classification for the purpose of tumor grading, prognosis analysis, etc. However, the design of token-wi…
Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis
Jun Li, Yushan Zheng, Kun Wu +3
Local representation learning has been a key challenge to promote the performance of the histopathological whole slide images analysis. The previous representation learning methods…
Histopathology WSI Encoding based on GCNs for Scalable and Efficient Retrieval of Diagnostically Relevant Regions
Yushan Zheng, Zhiguo Jiang, Haopeng Zhang +3
Content-based histopathological image retrieval (CBHIR) has become popular in recent years in the domain of histopathological image analysis. CBHIR systems provide auxiliary diagno…
Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019
Zhang Li, Jiehua Zhang, Tao Tan +30
Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in…