Pegah Khosravi

This paper introduces a weakly-supervised method to predict tumor purity from H&E stained slides, achieving high accuracy and enabling clinical applications.

This study uses deep learning to predict chromosomal instability (CIN) in breast cancer from histopathology images, achieving an AUC of 0.822 in a test set.

This paper develops an AI system to assess human blastocyst quality using deep learning, achieving 98% accuracy and identifying clinical factors affecting pregnancy rates.

Fine-tuned ImageNet ResNets beat Inception models on tissue micro-array and BreaKHis slides, reaching 99.8% across four tumour tissues and 96.4% across four malignant sub-types.

This study develops a CNN-based pipeline to classify histopathology images across cancer types and biomarkers, achieving high accuracy in distinguishing cancer subtypes and IHC scores.