Computer-aided Detection of Breast Cancer in Digital Tomosynthesis Imaging Using Deep and Multiple Instance Learning

Breast cancer is the most common cancer among women in the world. Nevertheless, early detection of breast cancer improves the chance of successful treatment. Digital breast tomosynthesis (DBT) as a new tomographic technique was developed to minimize the limitations of conventional digital mammography screening. A DBT is a quasi-three-dimensional image that is reconstructed from a small number of two-dimensional (2D) low-dose X-ray images. The 2D X-ray images are acquired over a limited angular around the breast. Our research aims to introduce computer-aided detection (CAD) frameworks to detect early signs of breast cancer in DBTs. In this thesis, we propose three CAD frameworks for detection of breast cancer in DBTs. The first CAD framework is based on hand-crafted feature extraction. Concerning early signs of breast cancer: mass, micro-calcifications, and bilateral asymmetry between left and right breast, the system includes three separate channels to detect each sign. Next two CAD frameworks automatically learn complex patterns of 2D slices using the deep convolutional neural network and the deep cardinality-restricted Boltzmann machines. Finally, the CAD frameworks employ a multiple-instance learning approach with randomized trees algorithm to classify DBT images based on extracted information from 2D slices. The frameworks operate on 2D slices which are generated from DBT volumes. These frameworks are developed and evaluated using 5,040 2D image slices obtained from 87 DBT volumes. We demonstrate the validation and usefulness of the proposed CAD frameworks within empirical experiments for detecting breast cancer in DBTs

Towards Interpretable Machine Learning in Medical Image Analysis

Over the past few years, ML has demonstrated human expert level performance in many medical image analysis tasks. However, due to the black-box nature of classic deep ML models, translating these models from the bench to the bedside to support the corresponding stakeholders in the desired tasks brings substantial challenges. One solution is interpretable ML, which attempts to reveal the working mechanisms of complex models. From a human-centered design perspective, interpretability is not a property of the ML model but an affordance, i.e., a relationship between algorithm and user. Thus, prototyping and user evaluations are critical to attaining solutions that afford interpretability. Following human-centered design principles in highly specialized and high stakes domains, such as medical image analysis, is challenging due to the limited access to end users. This dilemma is further exacerbated by the high knowledge imbalance between ML designers and end users. To overcome the predicament, we first define 4 levels of clinical evidence that can be used to justify the interpretability to design ML models. We state that designing ML models with 2 levels of clinical evidence: 1) commonly used clinical evidence, such as clinical guidelines, and 2) iteratively developed clinical evidence with end users are more likely to design models that are indeed interpretable to end users. In this dissertation, we first address how to design interpretable ML in medical image analysis that affords interpretability with these two different levels of clinical evidence. We further highly recommend formative user research as the first step of the interpretable model design to understand user needs and domain requirements. We also indicate the importance of empirical user evaluation to support transparent ML design choices to facilitate the adoption of human-centered design principles. All these aspects in this dissertation increase the likelihood that the algorithms afford interpretability and enable stakeholders to capitalize on the benefits of interpretable ML. In detail, we first propose neural symbolic reasoning to implement public clinical evidence into the designed models for various routinely performed clinical tasks. We utilize the routinely applied clinical taxonomy for abnormality classification in chest x-rays. We also establish a spleen injury grading system by strictly following the clinical guidelines for symbolic reasoning with the detected and segmented salient clinical features. Then, we propose the entire interpretable pipeline for UM prognostication with cytopathology images. We first perform formative user research and found that pathologists believe cell composition is informative for UM prognostication. Thus, we build a model to analyze cell composition directly. Finally, we conduct a comprehensive user study to assess the human factors of human-machine teaming with the designed model, e.g., whether the proposed model indeed affords interpretability to pathologists. The human-centered design process is proven to be truly interpretable to pathologists for UM prognostication. All in all, this dissertation introduces a comprehensive human-centered design for interpretable ML solutions in medical image analysis that affords interpretability to end users

Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further

Identificação e caracterização de genes codificantes de quitinase no genoma de Handroanthus impetiginosus

Chitinases are enzymes capable of hydrolyzing chitins, which are the second most abundant polymer on the planet, present in insect exoskeleton and fungal cell wall. Chitinases in plants are divided into two families of glycosyl hydrolases, families 18 and 19, and subdivided into classes I, II, III, IV and V. These enzymes have their main function related to protection against chitin-containing insects and pathogens. composition. Therefore, this trait can be used, with the help of genetic engineering, to generate pest resistant plants. Currently, genomics and bioinformatics make it possible to identify these resistance genes from the plant genome. The pink ipe (Handroanthus impetiginosus) is known for its great insect resistance. Therefore, the objective of this work was the identification and characterization of chitinases genes in the Handroanthus impetiginosus genome. Using the BLASTp, Blast2GO and MEGAX programs, a search for chitinase genes from families 18 and 19 in the H. impetiginosus genome was performed. These sequences were then confirmed to have family 18 or 19 glycosyl hydrolase catalytic domains. These sequences were aligned and analyzed by phylogenetic trees to classify these chitinases into the five known classes. The WoLF PSORT site was used to indicate the possible cellular location of chitinases and SWISS-MODEL was used to predict 3D structures. The searches resulted in 12 family 18 genes and 15 family 19 genes, all featuring the 3D domains and structure characteristic of chitinases.Trabalho de Conclusão de Curso (Graduação)Quitinases são enzimas capazes de hidrolisar quitinas, que são o segundo polímero mais abundante no planeta, presente no exoesqueleto de insetos e parede celular de fungos. As quitinases em plantas estão divididas em duas famílias de glicosil hidrolases, famílias 18 e 19, e subdivididas nas classes I, II, III, IV e V. Estas enzimas têm sua principal função relacionada a proteção contra insetos e patógenos que possuem quitina em sua composição. Portanto, essa característica pode ser utilizada, com o auxílio da engenharia genética, para gerar plantas resistentes as pragas. Atualmente, a genômica e a bioinformática possibilitam a identificação desses genes de resistência a partir do genoma de plantas. O ipê-rosa (Handroanthus impetiginosus) é conhecido por sua grande resistência a insetos. Portanto, o objetivo desse trabalho foi a identificação e caracterização de genes de quitinases no genoma de Handroanthus impetiginosus. Usando os programas BLASTp, Blast2GO e MEGAX foi realizada uma busca por genes de quitinases das famílias 18 e 19 no genoma de H. impetiginosus. Em seguida foram confirmadas se essas sequências possuíam domínios catalíticos de glicosil hidrolase das famílias 18 ou 19. Estas sequências foram alinhadas e analisadas por árvores filogenéticas, a fim de classificar essas quitinases dentro das cinco classes conhecidas. O site WoLF PSORT foi utilizado para indicar a possível localização celular das quitinases e o SWISS-MODEL foi utilizado para predição das estruturas 3D. As buscas resultaram em 12 genes da família 18 e 15 genes da família 19, todos apresentando os domínios e estrutura 3D características das quitinases

Evolving Artificial Neural Networks using Cartesian Genetic Programming

NeuroEvolution is the application of Evolutionary Algorithms to the training of Artificial Neural Networks. NeuroEvolution is thought to possess many benefits over traditional training methods including: the ability to train recurrent network structures, the capability to adapt network topology, being able to create heterogeneous networks of arbitrary transfer functions, and allowing application to reinforcement as well as supervised learning tasks. This thesis presents a series of rigorous empirical investigations into many of these perceived advantages of NeuroEvolution. In this work it is demonstrated that the ability to simultaneously adapt network topology along with connection weights represents a significant advantage of many NeuroEvolutionary methods. It is also demonstrated that the ability to create heterogeneous networks comprising a range of transfer functions represents a further significant advantage. This thesis also investigates many potential benefits and drawbacks of NeuroEvolution which have been largely overlooked in the literature. This includes the presence and role of genetic redundancy in NeuroEvolution's search and whether program bloat is a limitation. The investigations presented focus on the use of a recently developed NeuroEvolution method based on Cartesian Genetic Programming. This thesis extends Cartesian Genetic Programming such that it can represent recurrent program structures allowing for the creation of recurrent Artificial Neural Networks. Using this newly developed extension, Recurrent Cartesian Genetic Programming, and its application to Artificial Neural Networks, are demonstrated to be extremely competitive in the domain of series forecasting

Actas de SABI2020

Los temas salientes incluyen un marcapasos pulmonar que promete complementar y eventualmente sustituir la conocida ventilación mecánica por presión positiva (intubación), el análisis de la marchaespontánea sin costosos equipamientos, las imágenes infrarrojas y la predicción de la salud cardiovascular en temprana edad por medio de la biomecánica arterial

Classification of breast regions as mass and non-mass based on digital mammograms using taxonomic indexes and SVM

AbstractBreast cancer is the second most common type of cancer in the world. Several computer-aided detection and diagnosis systems have been used to assist health experts identify suspicious areas that are difficult to perceive with the human eye, thus aiding in the detection and diagnosis of cancer. This work proposes a methodology for the discrimination and classification of regions extracted from mammograms as mass and non-mass. The Digital Database for Screening Mammography (DDSM) was used in this work for the acquisition of mammograms. The taxonomic diversity index (Δ) and the taxonomic distinctness (Δ⁎), which were originally used in ecology, were used to describe the texture of the regions of interest. These indexes were computed based on phylogenetic trees, which were applied to describe the patterns in regions of breast images. Two approaches were used for the analysis of texture: internal and external masks. A support vector machine was used to classify the regions as mass and non-mass. The proposed methodology successfully classified the masses and non-masses, with an average accuracy of 98.88%