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
Integrated Fourier Transform Infrared Imaging and Proteomics for Identification of a Candidate Histochemical Biomarker in Bladder Cancer
X-Ray Micro-Computed Tomography for Nondestructive Three-Dimensional (3D) X-Ray Histology
Detection of Lung Cancer Lymph Node Metastases from Whole-Slide Histopathologic Images Using a Two-Step Deep Learning Approach
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
Image-Based Machine Learning Algorithms for Disease Characterization in the Human Type 1 Diabetes Pancreas
Three-Dimensional Vessel Segmentation in Whole-Tissue and Whole-Block Imaging Using a Deep Neural Network