5 citations · 8 across the 7 of their papers we have counts for
7 papers
PH2ST:ST-Prompt Guided Histological Hypergraph Learning for Spatial Gene Expression Prediction
Yi Niu, Jiashuai Liu, Yingkang Zhan +8
Spatial Transcriptomics (ST) reveals the spatial distribution of gene expression in tissues, offering critical insights into biological processes and disease mechanisms. However, t…
ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classification
Jiangbo Shi, Chen Li, Tieliang Gong +2
Multiple instance learning (MIL)-based framework has become the mainstream for processing the whole slide image (WSI) with giga-pixel size and hierarchical image context in digital…
Meta Mask Correction for Nuclei Segmentation in Histopathological Image
Jiangbo Shi, Chang Jia, Zeyu Gao +3
Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method re…
Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network
Zeyu Gao, Jiangbo Shi, Xianli Zhang +6
The grade of clear cell renal cell carcinoma (ccRCC) is a critical prognostic factor, making ccRCC nuclei grading a crucial task in RCC pathology analysis. Computer-aided nuclei gr…
Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images
Zeyu Gao, Pargorn Puttapirat, Jiangbo Shi +1
Obtaining a large amount of labeled data in medical imaging is laborious and time-consuming, especially for histopathology. However, it is much easier and cheaper to get unlabeled…
OpenHI2 -- Open source histopathological image platform
Pargorn Puttapirat, Haichuan Zhang, Jingyi Deng +7
Transition from conventional to digital pathology requires a new category of biomedical informatic infrastructure which could facilitate delicate pathological routine. Pathological…
Effects of annotation granularity in deep learning models for histopathological images
Jiangbo Shi, Zeyu Gao, Haichuan Zhang +4
Pathological is crucial to cancer diagnosis. Usually, Pathologists draw their conclusion based on observed cell and tissue structure on histology slides. Rapid development in machi…