Common Representation Learning Using Step-based Correlation Multi-Modal CNN

Deep learning techniques have been successfully used in learning a common representation for multi-view data, wherein the different modalities are projected onto a common subspace. In a broader perspective, the techniques used to investigate common representation learning falls under the categories of canonical correlation-based approaches and autoencoder based approaches. In this paper, we investigate the performance of deep autoencoder based methods on multi-view data. We propose a novel step-based correlation multi-modal CNN (CorrMCNN) which reconstructs one view of the data given the other while increasing the interaction between the representations at each hidden layer or every intermediate step. Finally, we evaluate the performance of the proposed model on two benchmark datasets - MNIST and XRMB. Through extensive experiments, we find that the proposed model achieves better performance than the current state-of-the-art techniques on joint common representation learning and transfer learning tasks.Comment: Accepted in Asian Conference of Pattern Recognition (ACPR-2017

Discriminative Representations for Heterogeneous Images and Multimodal Data

Histology images of tumor tissue are an important diagnostic and prognostic tool for pathologists. Recently developed molecular methods group tumors into subtypes to further guide treatment decisions, but they are not routinely performed on all patients. A lower cost and repeatable method to predict tumor subtypes from histology could bring benefits to more cancer patients. Further, combining imaging and genomic data types provides a more complete view of the tumor and may improve prognostication and treatment decisions. While molecular and genomic methods capture the state of a small sample of tumor, histological image analysis provides a spatial view and can identify multiple subtypes in a single tumor. This intra-tumor heterogeneity has yet to be fully understood and its quantification may lead to future insights into tumor progression. In this work, I develop methods to learn appropriate features directly from images using dictionary learning or deep learning. I use multiple instance learning to account for intra-tumor variations in subtype during training, improving subtype predictions and providing insights into tumor heterogeneity. I also integrate image and genomic features to learn a projection to a shared space that is also discriminative. This method can be used for cross-modal classification or to improve predictions from images by also learning from genomic data during training, even if only image data is available at test time.Doctor of Philosoph