Histopathology Image
Histopathology image analysis focuses on extracting meaningful information from microscopic images of tissue samples, primarily for disease diagnosis and prognosis. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), vision transformers (ViTs), and diffusion models for tasks such as image classification, segmentation, super-resolution, and cross-modality translation (e.g., H&E to IHC). These advancements aim to improve diagnostic accuracy, efficiency, and personalization of treatment, impacting both clinical practice and biomedical research by enabling more precise and objective assessments of tissue samples.
Papers
Glo-In-One-v2: Holistic Identification of Glomerular Cells, Tissues, and Lesions in Human and Mouse Histopathology
Lining Yu, Mengmeng Yin, Ruining Deng, Quan Liu, Tianyuan Yao, Can Cui, Junlin Guo, Yu Wang, Yaohong Wang, Shilin Zhao, Haichun Yang, Yuankai Huo
ST-Align: A Multimodal Foundation Model for Image-Gene Alignment in Spatial Transcriptomics
Yuxiang Lin, Ling Luo, Ying Chen, Xushi Zhang, Zihui Wang, Wenxian Yang, Mengsha Tong, Rongshan Yu
Evaluation Metric for Quality Control and Generative Models in Histopathology Images
Pranav Jeevan, Neeraj Nixon, Abhijeet Patil, Amit Sethi
PathoGen-X: A Cross-Modal Genomic Feature Trans-Align Network for Enhanced Survival Prediction from Histopathology Images
Akhila Krishna, Nikhil Cherian Kurian, Abhijeet Patil, Amruta Parulekar, Amit Sethi