F. M. Howard

Generative Adversarial Networks Accurately Reconstruct Pan-Cancer Histology from Pathologic, Genomic, and Radiographic Latent Features

This paper introduces HistoXGAN, a GAN that reconstructs cancer histology from latent features, showing it retains tumor grade, histologic subtype, and gene expression patterns across 29 cancer types.

Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers

This paper evaluates ChatGPT-generated scientific abstracts against original ones using AI detectors, plagiarism tools, and human reviewers, finding they are original but identifiable as AI-generated.

Validating a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning

The paper validates a low-cost, open-source workstation and computational pipeline for automated histopathology evaluation using deep learning, showing comparable performance to expensive institutional hardware.

Latent transcriptional programs reveal histology-encoded tumor features spanning tissue origins

This paper uses machine learning to identify latent transcriptional programs in squamous cell carcinomas from histology images, linking them to clinically relevant biological processes.

Multimodal Prediction of Breast Cancer Recurrence Assays and Risk of Recurrence

This paper develops a deep learning model that predicts breast cancer recurrence using digital histology and clinical factors, outperforming a clinical nomogram and identifying patients who may avoid genomic testing.