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18 papers · 1 filter
Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review
Ricardo Gonzalez, Peyman Nejat, Ashirbani Saha +3
Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important evidence of t…
Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities
Ricardo Gonzalez, Ashirbani Saha, Clinton J. V. Campbell +3
This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Syst…
MedYOLO: A Medical Image Object Detection Framework
Joseph Sobek, Jose R. Medina Inojosa, Betsy J. Medina Inojosa +4
Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional neural networks (CNNs) designed to…
Learning Anatomically Consistent Embedding for Chest Radiography
Ziyu Zhou, Haozhe Luo, Jiaxuan Pang +3
Self-supervised learning (SSL) approaches have recently shown substantial success in learning visual representations from unannotated images. Compared with photographic images, med…
A deformation-based morphometry framework for disentangling Alzheimer's disease from normal aging using learned normal aging templates
Jingru Fu, Daniel Ferreira, Örjan Smedby +1
Alzheimer's Disease and normal aging are both characterized by brain atrophy. The question of whether AD-related brain atrophy represents accelerated aging or a neurodegeneration p…
Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research
Bardia Khosravi, Frank Li, Theo Dapamede +8
Chest X-rays (CXR) are essential for diagnosing a variety of conditions, but when used on new populations, model generalizability issues limit their efficacy. Generative AI, partic…