Articles by Junhan Zhao
A unified vision-language model for precision oncology and biomarker prediction in neuroblastoma
Abstract Neuroblastoma is a leading cause of childhood cancer mortality, presenting persistent management challenges due to its biological heterogeneity and the limited accessibility of molecular profiling in routine practice. Here we present NEVA (NEuroblastoma Vision–language AI), a multimodal foundation model designed to address these barriers.
Contrastive learning enhances fairness in pathology artificial intelligence systems
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Results We collected 28,732 whole-slide cancer pathology images (WSIs) from 14,456 cancer patients, encompassing 20 cancer types across five medical centers and three nationwide study cohorts.
By Shih-Yen Lin, Pei-Chen Tsai, Fang-Yi Su, Chun-Yen Chen, Fuchen Li, Junhan Zhao, Tsung-Lu Michael Lee, Elizabeth Healey, Ting-Wan Kao, Thomas Roetzer-Pejrimovsky, Lynette M. Sholl, Deborah R. Dillon, Nancy Lin, David Meredith Verified, Keith L. Ligon, Ying-Chun Lo, Nipon Chaisuriya, Adelheid Woehrer, Jeffrey A. Meyerhardt, Shuji Ogino, MacLean P. Nasrallah, Jeffrey Golden, Sabina Signoretti, Jung-Hsien Chiang, Kun-Hsing Yu, Yuk Yeung Ho, Po-Jen Lin, Dmytro Vremenko
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Cell Reports Medicine
Verified
Physical activity levels may impact on the risk of premature mortality in people with epilepsy - Nature Communications
Abstract Epilepsy affects over 70 million individuals worldwide, with optimal physical activity (PA) levels remaining challenging to determine due to potential negative outcomes from both insufficient and excessive activity. To quantify the associations between objective PA and mortality in people with epilepsy, we analyzed accelerometer data from 98,561 UK Biobank participants, including 1167 with epilepsy, to quantify associations between objectively measured PA and mortality.
Uncertainty-aware ensemble of foundation models differentiates glioblastoma from its mimics - Nature Communications
Abstract Accurate pathological diagnosis is crucial in guiding personalized treatments for patients with central nervous system cancers. Distinguishing glioblastoma and primary central nervous system lymphoma is particularly challenging due to their overlapping pathology features, despite the distinct treatments required.
Pancancer outcome prediction via a unified weakly supervised deep learning model - Signal Transduction and Targeted Therapy
Abstract Accurate prognosis prediction is essential for guiding cancer treatment and improving patient outcomes. While recent studies have demonstrated the potential of histopathological images in survival analysis, existing models are typically developed in a cancer-specific manner, lack extensive external validation, and often rely on molecular data that are not routinely available in clinical practice.
Predicting the Distribution of Ailanthus altissima Using Deep Learning-Based Analysis of Satellite Imagery
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A pathology foundation model for cancer diagnosis and prognosis prediction - Nature
Abstract Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task1,2. Although such methods have achieved some success, they often have limited generalizability to images generated by different digitization protocols or samples collected from different populations3.
A systematic evaluation of computational methods for cell segmentation
Abstract Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation and instance segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images.
A systematic evaluation of computational methods for cell segmentation
Cell morphology, which describes the shape, size, and structure of cells, is crucial for understanding the types and states of cells and how cells respond to their microenvironments [1–7]. The study of cell morphology is enabled by biomedical imaging technologies such as phase-contrast microscopy [8, 9] and multiplexed fluorescence imaging [10].
A systematic evaluation of computation methods for cell segmentation
Abstract Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images.
Ultra‐processed food consumption and risk of dementia and Alzheimer's disease: Long‐term results from the Framingham Offspring Study
RESEARCH ARTICLE Kanran Wang and Junhan Zhao should be considered as joint senior authors. Abstract INTRODUCTION We aimed to assess the prospective associations between consumption of ultra-processed foods (UPF) and the risk of all-cause dementia and Alzheimer's disease (AD) dementia. METHOD This study included 2909 adult participants who were dementia free at baseline and had a follow-up. Dietary intakes were collected using the Food Frequency Questionnaire (FFQ).
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