Learning Opposites Using Neural Networks

Many research works have successfully extended algorithms such as evolutionary algorithms, reinforcement agents and neural networks using "opposition-based learning" (OBL). Two types of the "opposites" have been defined in the literature, namely \textit{type-I} and \textit{type-II}. The former are linear in nature and applicable to the variable space, hence easy to calculate. On the other hand, type-II opposites capture the "oppositeness" in the output space. In fact, type-I opposites are considered a special case of type-II opposites where inputs and outputs have a linear relationship. However, in many real-world problems, inputs and outputs do in fact exhibit a nonlinear relationship. Therefore, type-II opposites are expected to be better in capturing the sense of "opposition" in terms of the input-output relation. In the absence of any knowledge about the problem at hand, there seems to be no intuitive way to calculate the type-II opposites. In this paper, we introduce an approach to learn type-II opposites from the given inputs and their outputs using the artificial neural networks (ANNs). We first perform \emph{opposition mining} on the sample data, and then use the mined data to learn the relationship between input $x$ and its opposite $\breve{x}$. We have validated our algorithm using various benchmark functions to compare it against an evolving fuzzy inference approach that has been recently introduced. The results show the better performance of a neural approach to learn the opposites. This will create new possibilities for integrating oppositional schemes within existing algorithms promising a potential increase in convergence speed and/or accuracy.Comment: To appear in proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, December 201

Learning Discriminative Representations for Gigapixel Images

Digital images of tumor tissue are important diagnostic and prognostic tools for pathologists. Recent advancement in digital pathology has led to an abundance of digitized histopathology slides, called whole-slide images. Computational analysis of whole-slide images is a challenging task as they are generally gigapixel files, often one or more gigabytes in size. However, these computational methods provide a unique opportunity to improve the objectivity and accuracy of diagnostic interpretations in histopathology. Recently, deep learning has been successful in characterizing images for vision-based applications in multiple domains. But its applications are relatively less explored in the histopathology domain mostly due to the following two challenges. Firstly, there is difficulty in scaling deep learning methods for processing large gigapixel histopathology images. Secondly, there is a lack of diversified and labeled datasets due to privacy constraints as well as workflow and technical challenges in the healthcare sector. The main goal of this dissertation is to explore and develop deep models to learn discriminative representations of whole slide images while overcoming the existing challenges. A three-staged approach was considered in this research. In the first stage, a framework called Yottixel is proposed. It represents a whole-slide image as a set of multiple representative patches, called mosaic. The mosaic enables convenient processing and compact representation of an entire high-resolution whole-slide image. Yottixel allows faster retrieval of similar whole-slide images within large archives of digital histopathology images. Such retrieval technology enables pathologists to tap into the past diagnostic data on demand. Yottixel is validated on the largest public archive of whole-slide images (The Cancer Genomic Atlas), achieving promising results. Yottixel is an unsupervised method that limits its performance on specific tasks especially when the labeled (or partially labeled) dataset can be available. In the second stage, multi-instance learning (MIL) is used to enhance the cancer subtype prediction through weakly-supervised training. Three MIL methods have been proposed, each improving upon the previous one. The first one is based on memory-based models, the second uses attention-based models, and the third one uses graph neural networks. All three methods are incorporated in Yottixel to classify entire whole-slide images with no pixel-level annotations. Access to large-scale and diversified datasets is a primary driver of the advancement and adoption of machine learning technologies. However, healthcare has many restrictive rules around data sharing, limiting research and model development. In the final stage, a federated learning scheme called ProxyFL is developed that enables collaborative training of Yottixel among the multiple healthcare organizations without centralization of the sensitive medical data. The combined research in all the three stages of the Ph.D. has resulted in the development of a holistic and practical framework for learning discriminative and compact representations of whole-slide images in digital pathology

Content-based Image Retrieval of Gigapixel Histopathology Scans: A Comparative Study of Convolution Neural Network, Local Binary Pattern, and Bag of visual Words

The state-of-the-art image analysis algorithms offer a unique opportunity to extract semantically meaningful features from medical images. The advantage of this approach is automation in terms of content-based image retrieval (CBIR) of medical images. Such an automation leads to more reliable diagnostic decisions by clinicians as the direct beneficiary of these algorithms. Digital pathology (DP), or whole slide imaging (WSI), is a new avenue for image-based diagnosis in histopathology. WSI technology enables the digitization of traditional glass slides to ultra high-resolution digital images (or digital slides). Digital slides are more commonly used for CBIR research than other modalities of medical images due to their enormous size, increasing adoption among hospitals, and their various benefits offered to pathologists (e.g., digital telepathology). Pathology laboratories are under constant pressure to meet increasingly complex demands from hospitals. Many diseases (such as cancer) continue to grow which creates a pressing need to utilize existing innovative machine learning schemes to harness the knowledge contained in digital slides for more effective and efficient histopathology. This thesis provides a qualitative assessment of three popular image analysis techniques, namely Local Binary Pattern (LBP), Bag of visual Words (BoW), and Convolution Neural Networks (CNN) in their abilities to extract the discriminative features from gigapixel histopathology images. LBP and BoW are well-established techniques used in different image analysis problems. Over the last 5-10 years, CNN has become a frequent research topic in computer vision. CNN offers a domain-agnostic approach for the automatic extraction of discriminative image features, used for either classification or retrieval purposes. Therefore, it is imperative that this thesis gives more emphasis to CNN as a viable approach for the analysis of DP images. A new dataset, Kimia Path24 is specially designed and developed to facilitate the research in classification and CBIR of DP images. Kimia Path24 is used to measure the quality of image features extracted from LBP, BoW, and CNN; resulting in the best accuracy values of 41.33%, 54.67%, and 56.98% respectively. The results are somewhat surprising, suggesting that the handcrafted feature extraction algorithm, i.e., LBP can reach very close to the deep features extracted from CNN. It is unanticipated, considering that CNN requires much more computational resources and efforts for designing and fine-tuning. One of the conclusions is that CNN needs to be trained for the problem with a large number of training images to realize its comprehensive benefits. However, there are many situations where large, balanced, and the labeled dataset is not available; one such area is histopathology at present