8 citations · 12 across the 2 of their papers we have counts for
4 papers
Transcriptome-informed multi-modal AI for predicting neoadjuvant therapy response from breast cancer biopsies
Jungkyu Park, Dhruva Biswas, Joseph Cappadona +22
Scarcity of labeled data limits development of deep learning biomarkers in oncology. We develop a two-stage AI model predicting pathological complete response (pCR) to neoadjuvant…
Causal multi-modal AI for personalized chemosensitivity prediction
Dhruva Biswas, Jeroen Berrevoets, Alec McClean +31
Chemotherapy improves survival for some patients with breast cancer, but doctors cannot reliably predict who. Current guidelines rely on recurrence scores as a proxy for treatment…
Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization
James M. Dolezal, Sara Kochanny, Emma Dyer +6
Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environment…
Deep Learning Generates Synthetic Cancer Histology for Explainability and Education
James M. Dolezal, Rachelle Wolk, Hanna M. Hieromnimon +20
Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or excee…