Cross-Domain Knowledge Transfer for Prediction of Chemosensitivity in Ovarian Cancer Patients

Asfand Yaar, Amina Asif, Shan E Ahmed Raza, Nasir Rajpoot, Fayyaz Minhas; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 928-929

In this paper, we report a novel deep neural network framework for prediction of chemo-sensitivity in ovarian cancer patients. The proposed model is based on Multiple Instance Learning (MIL) and a novel variant of Learning using Privileged Information (LUPI). LUPI allows knowledge transfer from highly informative privileged features that are available only at training time to give improved generalization performance on input space features which are available in both training and inference. The proposed model is trained on image patches from Hematoxylin and Eosin (H&E) stained multi-gigapixel whole-slide images (WSIs, the input space) of ovarian cancer tissue sections and their associated gene expression profiles, the privileged feature space. Through cross-domain knowledge transfer with a novel combination of MIL and LUPI, we achieve improved generalization with a limited number of labeled examples in the input space. Informed by the privileged space model output based on relatively expensive and time-consuming gene expression profiles in its training, the proposed LUPI model can generate accurate predictions using routine WSI data alone at the time of inference. The proposed method paves the way for further applications of LUPI in computational pathology and medical image analysis by cross-domain learning especially in cases with a limited number of labeled examples in training.