Deep learning and localized features fusion for medical image classification

Local image features play an important role in many classification tasks as translation and rotation do not severely deteriorate the classification process. They have been commonly used for medical image analysis. In medical applications, it is important to get accurate diagnosis/aid results in the fastest time possible. This dissertation tries to tackle these problems, first by developing a localized feature-based classification system for medical images and using these features and to give a classification for the entire image, and second, by improving the computational complexity of feature analysis to make it viable as a diagnostic aid system in practical clinical situations. For local feature development, a new approach based on combining the rising deep learning paradigm with the use of handcrafted features is developed to classify cervical tissue histology images into different cervical intra-epithelial neoplasia classes. Using deep learning combined with handcrafted features improved the accuracy by 8.4% achieving 80.72% exact class classification accuracy compared to 72.29% when using the benchmark feature-based classification method --Abstract, page iv

Biophysical models of cis-regulation as interpretable neural networks

Abstract The adoption of deep learning techniques in genomics has been hindered by the difficulty of mechanistically interpreting the models that these techniques produce. In recent years, a variety of post-hoc attribution methods have been proposed for addressing this neural network interpretability problem in the context of gene regulation. Here we describe a complementary way of approaching this problem. Our strategy is based on the observation that two large classes of biophysical models of cis-regulatory mechanisms can be expressed as deep neural networks in which nodes and weights have explicit physiochemical interpretations. We also demonstrate how such biophysical networks can be rapidly inferred, using modern deep learning frameworks, from the data produced by certain types of massively parallel reporter assays (MPRAs). These results suggest a scalable strategy for using MPRAs to systematically characterize the biophysical basis of gene regulation in a wide range of biological contexts. They also highlight gene regulation as a promising venue for the development of scientifically interpretable approaches to deep learning

Machine Learning Applied to Raman Spectroscopy to Classify Cancers

Cancer diagnosis is notoriously difficult, evident in the inter-rater variability between histopathologists classifying cancerous sub-types. Although there are many cancer pathologies, they have in common that earlier diagnosis would maximise treatment potential. To reduce this variability and expedite diagnosis, there has been a drive to arm histopathologists with additional tools. One such tool is Raman spectroscopy, which has demonstrated potential in distinguishing between various cancer types. However, Raman data has high dimensionality and often contains artefacts and together with challenges inherent to medical data, classification attempts can be frustrated. Deep learning has recently emerged with the promise of unlocking many complex datasets, but it is not clear how this modelling paradigm can best exploit Raman data for cancer diagnosis. Three Raman oncology datasets (from ovarian, colonic and oesophageal tissue) were used to examine various methodological challenges to machine learning applied to Raman data, in conjunction with a thorough review of the recent literature. The performance of each dataset is assessed with two traditional and one deep learning models. A technique is then applied to the deep learning model to aid interpretability and relate biochemical antecedents to disease classes. In addition, a clinical problem for each dataset was addressed, including the transferability of models developed using multi-centre Raman data taken different on spectrometers of the same make. Many subtleties of data processing were found to be important to the realistic assessment of a machine learning models. In particular, appropriate cross-validation during hyperparameter selection, splitting data into training and test sets according to the inherent structure of biomedical data and addressing the number of samples Abstract " per disease class are all found to be important factors. Additionally, it was found that instrument correction was not needed to ensure system transferability if Raman data is collected with a common protocol on spectrometers of the same make

Multi-Class Cancer Subtyping in Salivary Gland Carcinomas with MALDI Imaging and Deep Learning

Simple Summary The correct diagnosis of different salivary gland carcinomas is important for a prognosis. This diagnosis is imprecise if it is based only on clinical symptoms and histological methods. Mass spectrometry imaging can provide information about the molecular composition of sample tissues. Using a deep-learning method, we analyzed the mass spectrometry imaging data of 25 patients. Using this workflow we could accurately predict the tumor type in each patient sample. Abstract Salivary gland carcinomas (SGC) are a heterogeneous group of tumors. The prognosis varies strongly according to its type, and even the distinction between benign and malign tumor is challenging. Adenoid cystic carcinoma (AdCy) is one subgroup of SGCs that is prone to late metastasis. This makes accurate tumor subtyping an important task. Matrix-assisted laser desorption/ionization (MALDI) imaging is a label-free technique capable of providing spatially resolved information about the abundance of biomolecules according to their mass-to-charge ratio. We analyzed tissue micro arrays (TMAs) of 25 patients (including six different SGC subtypes and a healthy control group of six patients) with high mass resolution MALDI imaging using a 12-Tesla magnetic resonance mass spectrometer. The high mass resolution allowed us to accurately detect single masses, with strong contributions to each class prediction. To address the added complexity created by the high mass resolution and multiple classes, we propose a deep-learning model. We showed that our deep-learning model provides a per-class classification accuracy of greater than 80% with little preprocessing. Based on this classification, we employed methods of explainable artificial intelligence (AI) to gain further insights into the spectrometric features of AdCys

Maximizing the Benefits of Collaborative Learning in the College Classroom

abstract: This study tested the effects of two kinds of cognitive, domain-based preparation tasks on learning outcomes after engaging in a collaborative activity with a partner. The collaborative learning method of interest was termed "preparing-to-interact," and is supported in theory by the Preparation for Future Learning (PFL) paradigm and the Interactive-Constructive-Active-Passive (ICAP) framework. The current work combined these two cognitive-based approaches to design collaborative learning activities that can serve as alternatives to existing methods, which carry limitations and challenges. The "preparing-to-interact" method avoids the need for training students in specific collaboration skills or guiding/scripting their dialogic behaviors, while providing the opportunity for students to acquire the necessary prior knowledge for maximizing their discussions towards learning. The study used a 2x2 experimental design, investigating the factors of Preparation (No Prep and Prep) and Type of Activity (Active and Constructive) on deep and shallow learning. The sample was community college students in introductory psychology classes; the domain tested was "memory," in particular, concepts related to the process of remembering/forgetting information. Results showed that Preparation was a significant factor affecting deep learning, while shallow learning was not affected differently by the interventions. Essentially, equalizing time-on-task and content across all conditions, time spent individually preparing by working on the task alone and then discussing the content with a partner produced deeper learning than engaging in the task jointly for the duration of the learning period. Type of Task was not a significant factor in learning outcomes, however, exploratory analyses showed evidence of Constructive-type behaviors leading to deeper learning of the content. Additionally, a novel method of multilevel analysis (MLA) was used to examine the data to account for the dependency between partners within dyads. This work showed that "preparing-to-interact" is a way to maximize the benefits of collaborative learning. When students are first cognitively prepared, they seem to make the most efficient use of discussion towards learning, engage more deeply in the content during learning, leading to deeper knowledge of the content. Additionally, in using MLA to account for subject nonindependency, this work introduces new questions about the validity of statistical analyses for dyadic data.Dissertation/ThesisPh.D. Educational Psychology 201

Fusion of Mini-Deep Nets

Image classification and object recognition are some of the most prominent problems in computer vision. The difficult nature of finding objects regardless of pose and occlusions requires a large number of compute resources. Recent advancements in technology have made great strides towards solving this problem, and in particular, deep learning has revolutionized this field in the last few years. The classification of large datasets, such as the popular ImageNet dataset, requires a network with millions of weights. Learning each of these weights using back propagation requires a compute intensive training phase with many training samples. Recent compute technology has proven adept at classifying 1000 classes, but it is not clear if computers will be able to differentiate and classify the more than 40,000 classes humans are capable of doing. The goal of this thesis is to train computers to attain human-like performance on large-class datasets. Specifically, we introduce two types of hierarchical architectures: Late Fusion and Early Fusion. These architectures will be used to classify datasets with up to 1000 objects, while simultaneously reducing both the number of computations and training time. These hierarchical architectures maintain discriminative relationships amongst networks within each layer as well as an abstract relationship from one layer to the next. The resulting framework reduces the individual network sizes, and thus the total number of parameters that need to be learned. The smaller number of parameters results in decreased training time