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Toward efficient slide-level grading of liver biopsy via explainable deep learning framework.
Med Biol Eng Comput
January 2025
Pathology Department, Beijing Youan Hospital, Capital Medical University, Beijing, 100000, China.
In the context of chronic liver diseases, where variability in progression necessitates early and precise diagnosis, this study addresses the limitations of traditional histological analysis and the shortcomings of existing deep learning approaches. A novel patch-level classification model employing multi-scale feature extraction and fusion was developed to enhance the grading accuracy and interpretability of liver biopsies, analyzing 1322 cases across various staining methods. The study also introduces a slide-level aggregation framework, comparing different diagnostic models, to efficiently integrate local histological information.
View Article and Find Full Text PDFKMeansGraphMIL: A Weakly Supervised Multiple Instance Learning Model for Predicting Colorectal Cancer Tumor Mutational Burden.
Am J Pathol
January 2025
The Seventh Affiliated Hospital, Sun Yat-Sen University, 628 Zhenyuan Road, Xinhu Street, Guangming New District, Shenzhen, 518107, Guangdong, China. Electronic address:
Colorectal cancer (CRC) is one of the top three most lethal malignancies worldwide, posing a significant threat to human health. Recently proposed immunotherapy checkpoint blockade treatments have proven effective for CRC, but their use depends on measuring specific biomarkers in patients. Among these biomarkers, Tumor Mutational Burden (TMB) has emerged as a novel indicator, traditionally requiring Next-Generation Sequencing (NGS) for measurement, which is time-consuming, labor-intensive, and costly.
View Article and Find Full Text PDFGraph neural networks in histopathology: Emerging trends and future directions.
Med Image Anal
January 2025
Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands; Amsterdam University Medical Center, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
Histopathological analysis of whole slide images (WSIs) has seen a surge in the utilization of deep learning methods, particularly Convolutional Neural Networks (CNNs). However, CNNs often fail to capture the intricate spatial dependencies inherent in WSIs. Graph Neural Networks (GNNs) present a promising alternative, adept at directly modeling pairwise interactions and effectively discerning the topological tissue and cellular structures within WSIs.
View Article and Find Full Text PDFDesigning of immunodiagnostic assay using polyclonal antibodies for detection of Enteropathogenic Escherichia coli strains.
PLoS One
January 2025
Institute of Biotechnology, Addis Ababa University, Addis Ababa, Ethiopia.
Enteropathogenic Escherichia coli (EPEC) is a significant bacterial pathogen that causes infantile diarrhea, particularly in low- and middle-income countries. The lack of a reliable diagnostic method greatly contributes to the increased occurrence and severity of the disease. This study aimed at developing of a cost-effective, rapid, and efficient immunodiagnostic assay for detecting EPEC infection.
View Article and Find Full Text PDFSame-Slide Spatial Multi-Omics Integration Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment.
bioRxiv
December 2024
Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.
The advent of spatial transcriptomics and spatial proteomics have enabled profound insights into tissue organization to provide systems-level understanding of diseases. Both technologies currently remain largely independent, and emerging same slide spatial multi-omics approaches are generally limited in plex, spatial resolution, and analytical approaches. We introduce IN-situ DEtailed Phenotyping To High-resolution transcriptomics (IN-DEPTH), a streamlined and resource-effective approach compatible with various spatial platforms.
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