Residual Disease

Residual disease (RD), the persistence of cancer cells after treatment, is a critical factor influencing cancer prognosis and treatment decisions. Current research focuses on developing advanced methods for detecting and quantifying RD, leveraging techniques like flow cytometry analysis with optimal transport-based dimensionality reduction and deep learning approaches for image registration and multi-view analysis of 3D medical images (e.g., CT scans). These improved detection methods aim to enhance the accuracy of RD assessment, leading to more personalized and effective cancer therapies and ultimately improving patient outcomes.

Papers

July 24, 2024

Low dimensional representation of multi-patient flow cytometry datasets using optimal transport for minimal residual disease detection in leukemia
Erell Gachon, Jérémie Bigot, Elsa Cazelles, Audrey Bidet, Jean-Philippe Vial, Pierre-Yves Dumas, Aguirre Mimoun
Optimal Transport Low Dimensional Representation Flow Cytometry Acute Myeloid Leukemia Residual Disease

January 18, 2024

BreastRegNet: A Deep Learning Framework for Registration of Breast Faxitron and Histopathology Images
Negar Golestani, Aihui Wang, Gregory R Bean, Mirabela Rusu
Image Registration Deep Learning Framework Breast Cancer Histopathology Image Pre Registration Breast Cancer Detection Residual Disease

June 26, 2023

Multi-View Attention Learning for Residual Disease Prediction of Ovarian Cancer
Xiangneng Gao, Shulan Ruan, Jun Shi, Guoqing Hu, Wei Wei
Medical Image 3d Ct Multi View Attention Ovarian Cancer Residual Disease

Residual Disease

Papers

Low dimensional representation of multi-patient flow cytometry datasets using optimal transport for minimal residual disease detection in leukemia

BreastRegNet: A Deep Learning Framework for Registration of Breast Faxitron and Histopathology Images

Multi-View Attention Learning for Residual Disease Prediction of Ovarian Cancer