C. M. L. Loeffler

Multimodal histopathologic models stratify hormone receptor-positive early breast cancer

This paper develops a multimodal transformer model using H&E images and text reports to predict recurrence scores in hormone receptor-positive breast cancer, achieving high accuracy and generalizability.

Self-supervised deep learning for pan-cancer mutation prediction from histopathology

This paper develops a self-supervised deep learning pipeline to predict genetic mutations from histopathology images across seven cancer types in two large datasets.

DeepMed: A unified, modular pipeline for end-to-end deep learning in computational pathology

DeepMed provides a modular, scalable AI pipeline for predicting molecular and histopathological features from histopathology images, with public benchmark data.

Classical Mathematical Models for Prediction of Response to Chemotherapy and Immunotherapy

This study evaluates classical mathematical models for predicting chemotherapy and immunotherapy responses using real-world patient tumor volume data, finding the Gompertz model provides the best balance between fit and complexity.