Biomedical Data Classification with Improvised Deep Learning Architectures

With the rise of very powerful hardware and evolution of deep learning architectures, healthcare data analysis and its applications have been drastically transformed. These transformations mainly aim to aid a healthcare personnel with diagnosis and prognosis of a disease or abnormality at any given point of healthcare routine workflow. For instance, many of the cancer metastases detection depends on pathological tissue procedures and pathologist reviews. The reports of severity classification vary amongst different pathologist, which then leads to different treatment options for a patient. This labor-intensive work can lead to errors or mistreatments resulting in high cost of healthcare. With the help of machine learning and deep learning modules, some of these traditional diagnosis techniques can be improved and aid a doctor in decision making with an unbiased view. Some of such modules can help reduce the cost, shortage of an expertise, and time in identifying the disease. However, there are many other datapoints that are available with medical images, such as omics data, biomarker calculations, patient demographics and history. All these datapoints can enhance disease classification or prediction of progression with the help of machine learning/deep learning modules. However, it is very difficult to find a comprehensive dataset with all different modalities and features in healthcare setting due to privacy regulations. Hence in this thesis, we explore both medical imaging data with clinical datapoints as well as genomics datasets separately for classification tasks using combinational deep learning architectures. We use deep neural networks with 3D volumetric structural magnetic resonance images of Alzheimer Disease dataset for classification of disease. A separate study is implemented to understand classification based on clinical datapoints achieved by machine learning algorithms. For bioinformatics applications, sequence classification task is a crucial step for many metagenomics applications, however, requires a lot of preprocessing that requires sequence assembly or sequence alignment before making use of raw whole genome sequencing data, hence time consuming especially in bacterial taxonomy classification. There are only a few approaches for sequence classification tasks that mainly involve some convolutions and deep neural network. A novel method is developed using an intrinsic nature of recurrent neural networks for 16s rRNA sequence classification which can be adapted to utilize read sequences directly. For this classification task, the accuracy is improved using optimization techniques with a hybrid neural network

A Comprehensive Review on Computer Vision Analysis of Aerial Data

With the emergence of new technologies in the field of airborne platforms and imaging sensors, aerial data analysis is becoming very popular, capitalizing on its advantages over land data. This paper presents a comprehensive review of the computer vision tasks within the domain of aerial data analysis. While addressing fundamental aspects such as object detection and tracking, the primary focus is on pivotal tasks like change detection, object segmentation, and scene-level analysis. The paper provides the comparison of various hyper parameters employed across diverse architectures and tasks. A substantial section is dedicated to an in-depth discussion on libraries, their categorization, and their relevance to different domain expertise. The paper encompasses aerial datasets, the architectural nuances adopted, and the evaluation metrics associated with all the tasks in aerial data analysis. Applications of computer vision tasks in aerial data across different domains are explored, with case studies providing further insights. The paper thoroughly examines the challenges inherent in aerial data analysis, offering practical solutions. Additionally, unresolved issues of significance are identified, paving the way for future research directions in the field of aerial data analysis.Comment: 112 page

A new evolutionary search strategy for global optimization of high-dimensional problems

Global optimization of high-dimensional problems in practical applications remains a major challenge to the research community of evolutionary computation. The weakness of randomization-based evolutionary algorithms in searching high-dimensional spaces is demonstrated in this paper. A new strategy, SP-UCI is developed to treat complexity caused by high dimensionalities. This strategy features a slope-based searching kernel and a scheme of maintaining the particle population's capability of searching over the full search space. Examinations of this strategy on a suite of sophisticated composition benchmark functions demonstrate that SP-UCI surpasses two popular algorithms, particle swarm optimizer (PSO) and differential evolution (DE), on high-dimensional problems. Experimental results also corroborate the argument that, in high-dimensional optimization, only problems with well-formative fitness landscapes are solvable, and slope-based schemes are preferable to randomization-based ones. © 2011 Elsevier Inc. All rights reserved

Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challenge

Training a deep neural network is an optimization problem with four main ingredients: the design of the deep neural network, the per-sample loss function, the population loss function, and the optimizer. However, methods developed to compete in recent BraTS challenges tend to focus only on the design of deep neural network architectures, while paying less attention to the three other aspects. In this paper, we experimented with adopting the opposite approach. We stuck to a generic and state-of-the-art 3D U-Net architecture and experimented with a non-standard per-sample loss function, the generalized Wasserstein Dice loss, a non-standard population loss function, corresponding to distributionally robust optimization, and a non-standard optimizer, Ranger. Those variations were selected specifically for the problem of multi-class brain tumor segmentation. The generalized Wasserstein Dice loss is a per-sample loss function that allows taking advantage of the hierarchical structure of the tumor regions labeled in BraTS. Distributionally robust optimization is a generalization of empirical risk minimization that accounts for the presence of underrepresented subdomains in the training dataset. Ranger is a generalization of the widely used Adam optimizer that is more stable with small batch size and noisy labels. We found that each of those variations of the optimization of deep neural networks for brain tumor segmentation leads to improvements in terms of Dice scores and Hausdorff distances. With an ensemble of three deep neural networks trained with various optimization procedures, we achieved promising results on the validation dataset of the BraTS 2020 challenge. Our ensemble ranked fourth out of the 693 registered teams for the segmentation task of the BraTS 2020 challenge.Comment: MICCAI 2020 BrainLes Workshop. Our method ranked fourth out of the 693 registered teams for the segmentation task of the BraTS 2020 challenge. v2: Added some clarifications following reviewers' feedback (camera-ready version