Machine Learning, Computational Pathology, and Biophysical Imaging

 Explore AJP’s newest topic category, introduced in 2019. The first article in this compendium is an Editorial that describes the genesis of and rationale for this category, formed to encompass the growing body of studies that apply artificial intelligence to issues in pathology. The full text of the majority of these articles is available to all readers at no cost.

Curated Collections From The American Journal of Pathology
Artificial Intelligence and Pathobiology Join Forces in The American Journal of Pathology
Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer
Computer-Aided Pathologic Diagnosis of Nasopharyngeal Carcinoma Based on Deep Learning
Decidual Vasculopathy Identification in Whole Slide Images Using Multiresolution Hierarchical Convolutional Neural Networks
Pathology of Idiopathic Pulmonary Fibrosis Assessed by a Combination of Microcomputed Tomography, Histology, and Immunohistochemistry
A Deep Learning-Based Approach for Glomeruli Instance Segmentation from Multistained Renal Biopsy Pathologic Images
Deep-Learning–Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies
Mouse Mammary Gland Whole Mount Density Assessment across Different Morphologies Using a Bifurcated Program for Image Processing
Convolutional Neural Networks for the Evaluation of Chronic and Inflammatory Lesions in Kidney Transplant Biopsies
Differential Diagnosis of Hematologic and Solid Tumors Using Targeted Transcriptome and Artificial Intelligence
Deep Domain Adversarial Learning for Species-Agnostic Classification of Histologic Subtypes of Osteosarcoma
Deep Learning–Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction