Innovations in Medical Image Analysis and Explainable AI for Transparent Clinical Decision Support Systems

This thesis explores innovative methods designed to assist clinicians in their everyday practice, with a particular emphasis on Medical Image Analysis and Explainability issues. The main challenge lies in interpreting the knowledge gained from machine learning algorithms, also called black-boxes, to provide transparent clinical decision support systems for real integration into clinical practice. For this reason, all work aims to exploit Explainable AI techniques to study and interpret the trained models. Given the countless open problems for the development of clinical decision support systems, the project includes the analysis of various data and pathologies. The main works are focused on the most threatening disease afflicting the female population: Breast Cancer. The works aim to diagnose and classify breast cancer through medical images by taking advantage of a first-level examination such as Mammography screening, Ultrasound images, and a more advanced examination such as MRI. Papers on Breast Cancer and Microcalcification Classification demonstrated the potential of shallow learning algorithms in terms of explainability and accuracy when intelligible radiomic features are used. Conversely, the union of deep learning and Explainable AI methods showed impressive results for Breast Cancer Detection. The local explanations provided via saliency maps were critical for model introspection, as well as increasing performance. To increase trust in these systems and aspire to their real use, a multi-level explanation was proposed. Three main stakeholders who need transparent models have been identified: developers, physicians, and patients. For this reason, guided by the enormous impact of COVID-19 in the world population, a fully Explainable machine learning model was proposed for COVID-19 Prognosis prediction exploiting the proposed multi-level explanation. It is assumed that such a system primarily requires two components: 1) inherently explainable inputs such as clinical, laboratory, and radiomic features; 2) Explainable methods capable of explaining globally and locally the trained model. The union of these two requirements allows the developer to detect any model bias, the doctor to verify the model findings with clinical evidence, and justify decisions to patients. These results were also confirmed for the study of coronary artery disease. In particular machine learning algorithms are trained using intelligible clinical and radiomic features extracted from pericoronaric adipose tissue to assess the condition of coronary arteries. Eventually, some important national and international collaborations led to the analysis of data for the development of predictive models for some neurological disorders. In particular, the predictivity of handwriting features for the prediction of depressed patients was explored. Using the training of neural networks constrained by first-order logic, it was possible to provide high-performance and explainable models, going beyond the trade-off between explainability and accuracy

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images

In this study, the main objective is to develop an algorithm capable of identifying and delineating tumor regions in breast ultrasound (BUS) and mammographic images. The technique employs two advanced deep learning architectures, namely U-Net and pretrained SAM, for tumor segmentation. The U-Net model is specifically designed for medical image segmentation and leverages its deep convolutional neural network framework to extract meaningful features from input images. On the other hand, the pretrained SAM architecture incorporates a mechanism to capture spatial dependencies and generate segmentation results. Evaluation is conducted on a diverse dataset containing annotated tumor regions in BUS and mammographic images, covering both benign and malignant tumors. This dataset enables a comprehensive assessment of the algorithm's performance across different tumor types. Results demonstrate that the U-Net model outperforms the pretrained SAM architecture in accurately identifying and segmenting tumor regions in both BUS and mammographic images. The U-Net exhibits superior performance in challenging cases involving irregular shapes, indistinct boundaries, and high tumor heterogeneity. In contrast, the pretrained SAM architecture exhibits limitations in accurately identifying tumor areas, particularly for malignant tumors and objects with weak boundaries or complex shapes. These findings highlight the importance of selecting appropriate deep learning architectures tailored for medical image segmentation. The U-Net model showcases its potential as a robust and accurate tool for tumor detection, while the pretrained SAM architecture suggests the need for further improvements to enhance segmentation performance

Matching of Mammographic Lesions in Different Breast Projections

De todos os cancros, cancro da mama é o que causa mais mortes entre mulheres. Programas de rastreio do cancro da mama podem ajudar a decrescer esta mortalidade, visto que deteção e tratamento do tumor em fases iniciais aumentam a taxa de sobrevivência. Normalmente, um par de radiologistas fazem a interpretação das mamografias, no entanto o processo é longo e cansativo. Isto incentivou o desenvolvimento de sistemas de diagnósitco auxiliado por computador (CADx), para substituir o segundo radiologista, fazendo melhor uso do tempo de especialistas. No entanto, sistemas CADx são associados a taxas elevadas de falsos positivos, dado que a maior parte detes apenas usam uma vista (craniocaudal ou mediolateral oblique) da mamografia. O radiologista, por sua vez, usa ambas as projeções, baseando o seu diagnóstico em diferenças visíveis entre as duas vistas. Quando se consideram as duas projeções da mamografia, a correspondência de lesões é um passo necessário para se fazer o diagnóstico. No entanto, isto é uma tarefa complexa, dado que podem existir vários candidatos a lesão, em cada uma das vistas, para se fazer correspondência. Neste trabalho, um sistema que faz correspondências entre lesões é proposto. Este é composto por três blocos: detetor de candidatos, extração de caraterísticas e correspondência de lesões. O primeiro é uma replicação do trabalho de Ribli et al., e o seu propósito é detetar possíveis candidatos a lesão. O segundo é a extração de vetores de caraterísticas de cada candidato, quer usando a backbone do detetor de candidatos, quer extraindo caraterísticas mais tradicionais, ou usando uma rede neuronal treinada com a triplet loss para distinguir lesões. O terceiro é o cálculo da distância entre os vetores de caraterísticas, usando também heurísticas para restringir possíveis pares de candidatos incorretos, e a ordenação de distâncias para atribuir a correspondência de cada lesão. Este trabalho oferece várias opções de possíveis extractores de caraterísticas e heurísticas a serem incroporados num sistema CADx que seja baseado em detetores de objetos. O facto do modelo treinado com a triplet loss ser competitivo com os restantos modelos, torna o sistema bastante mais viável, sendo que este oferece a possibilidade de a correspondência ser independente da deteção de candidatos. Heurísticas "hard" e "soft" são introduzidas como métodos para limitar correspondências. O sistema é capaz de fazer correspondências de forma satisfatória, dado que a sua exatidão ( 70%85%) é significativamente maior que a probabilidade aleatória (30%40%) dos dados usados. Heurísticas "hard" têm resultados encorajantes na precision@k, dado que estas rejeitam um número significativo de falsos positivos gerados pelo detetor de lesões.Of all cancer diseases, breast cancer is the most lethal among women. It has been shown that breast cancer screening programs can decrease mortality, since early detection increases the chances of survival. Usually, a pair of radiologists interpret the screening mammograms, however the process is long and exhausting. This has encouraged the development of computer aided diagnosis (CADx) systems to replace the second radiologist, making a better use of human-experts' time. But CADx systems are associated with high false positive rates, since most of them only use one view (craniocaudal or mediolateral oblique) of the screening mammogram. Radiologist, on the other hand, use both views; frequently reasoning about the diagnosis by noticeable differences between the two views. When considering both projections of a mammogram, lesion matching is a necessary step to perform diagnosis. However this is a complex task, since there might be various lesion candidates on both projections to match. In this work, a matching system is proposed. The system is a cascade of three blocks: candidates detector, feature extraction and lesion matching. The first is a replication of Ribli et al.'s Faster R-CNN and its purpose is to find possible lesion candidates. The second is the feature vector extraction of each candidate, either by using the candidates detector's backbone, handcrafted features or a siamese network model trained for distinguish lesions. The third is the calculus of the distance between feature vector, also using some heuristics to restrain possible non-lesion pairs, and the ranking of the distances to match the lesions. This work provides several options of possible feature extractors and heuristics to be incorporated into a CADx system based on object detectors. The fact that the triplet loss trained models obtained competitive results with the other features extractors is valuable, since it offers some independence between the detection and matching tasks. "Hard" heuristics and "soft" heurisitcs are introduced as methods to restrain matching. The system is able to detect matches satisfactorily, since its accuracy (70%85%) is significantly higher than chance level (30%40%). "Hard" heuristics proposals achieved encouraging results on precision@k, due to its match and candidates exclusion methods, which rejects a significant number of false positives generated by the object detector

Methodology for taking a computer-aided breast cancer screening system from the laboratory to the marketplace

Breast cancer is one of the most common causes of death in women, and yet is one of the more 'curable' cancers if caught early. Since its inception in 1987, the Breast Screening Programme has been the principal tool in the National Health Service's fight to reduce the number of cancer related deaths in the UK. Breast screening using mammography is widely viewed as the most effective way of detecting early breast cancer, with the UK population of women over the age of 50 being invited to a screening session every three years. However, national shortages of clinical staff willing to enter and remain in this field mean that the NHS Breast Screening Programme is severely understaffed. This thesis discusses one way in which technology can assist in the screening programme; specifically, the use of a computer-aided cancer detection system. Here, we will present the design and analysis of a sequence of experiments used to develop and evaluate such a system. PROMAM (PROmpting for MAMmography) involved the scanning and digitising of mammograms, and the subsequent analysis of the digital image by a series of algorithms. Initial evaluation was done to ensure that the algorithms were performing satisfactorily at a technical level before being introduced into a clinical setting. Two large experiments with the algorithms were designed and evaluated: 1. offering radiologists three levels of algorithm prompting and, as a control, an unprompted level, on samples of mammographic films, with outcomes being their recall rate and subjective views at each prompting level, 2. a pre-clinical experiment, conducted under semi-clinical conditions, where two readers would see a batch of films seeded with higher than normal numbers of cancers, with readers allocated randomly to prompted and unprompted views of films. The first experiment was designed using a Graeco-Latin Square, with three 'nuisance' variables and the treatment factor of prompting levels (no prompts, low level of prompt¬ ing, medium and high). Four radiologists read at each level of prompting once, on dif¬ ferent sets of films. One of the more interesting results was that the recall rate did not increase as the prompting rate rose - contrary to prior expectations. Most of the differ¬ ences seen between the prompting rates could be explained as radiologist differences. Once these were taken into account, the level of prompting had little effect. Addition¬ ally, although the time taken to read a set of films increased as the prompting rate increased (as would be expected), it was only an increase of 26% from the unprompted set to the set with the highest number of prompts. Observational data suggested that the lowest level of prompting was not maintaining the interest of the radiologist, thus leading them to neglect the prompts. The following experiment moved the system a step closer to a true clinical demonstra¬ tion of the efficacy of PROMAM, being conducted under semi-clinical conditions. Using a method of minimisation, the number of cancers each radiologist viewed as first reader, second reader, prompted or unprompted were balanced. Preliminary exploratory anal¬ ysis indicated that the recall rate declined with the introduction of the prompting system, but more detailed, analysis indicated that much of this difference was due to a radiologist effect. Although cancer detection was slightly lower with the prompting system, examination of the 11 cancers missed by the prompted radiologist showed that six of these had been correctly prompted by the algorithms. This demonstrated scope to improve the cancer detection rate by nearly 5%. These experiments determined the 'production' version of the prompting system. A design to evaluate the system in a sample of 100,000 women in six centres was produced, but due to circumstances beyond the project team's control, it was not possible to take this work to the stage of a full 'trial' of the system. The design concept can, however, apply to the evaluation of any similar prompting system. The recommended design is therefore presented, together with an analysis of data from a simulated application of this design. This simulation has allowed recommendations to be made on the most appropriate ways to analyse the extensive and complicated dataset that will be obtained. In particular, it identified technical problems that can arise from the application on one candidate analytical method, and an explanation for the failure obtained It is quite clear from the evidence presented in this thesis that there is much scope for improvement in the cancer detection rate by the use of a prompting system, with¬ out a corresponding loss in the specificity. With the shortage of radiologists and ra¬ diographers, and the increasing demand placed on the Breast Screening Programme, technology could play a beneficial role in screening for breast cancer in the coming year

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models

Mammography Techniques and Review

Mammography remains at the backbone of medical tools to examine the human breast. The early detection of breast cancer typically uses adjunct tests to mammogram such as ultrasound, positron emission mammography, electrical impedance, Computer-aided detection systems and others. In the present digital era it is even more important to use the best new techniques and systems available to improve the correct diagnosis and to prevent mortality from breast cancer. The first part of this book deals with the electrical impedance mammographic scheme, ultrasound axillary imaging, position emission mammography and digital mammogram enhancement. A detailed consideration of CBR CAD System and the availability of mammographs in Brazil forms the second part of this book. With the up-to-date papers from world experts, this book will be invaluable to anyone who studies the field of mammography

A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis

[EN]This paper proposes a machine learning approach dealing with genetic programming to build classifiers through logical rule induction. In this context, we define and test a set of mutation operators across from different clinical datasets to improve the performance of the proposal for each dataset. The use of genetic programming for rule induction has generated interesting results in machine learning problems. Hence, genetic programming represents a flexible and powerful evolutionary technique for automatic generation of classifiers. Since logical rules disclose knowledge from the analyzed data, we use such knowledge to interpret the results and filter the most important features from clinical data as a process of knowledge discovery. The ultimate goal of this proposal is to provide the experts in the data domain with prior knowledge (as a guide) about the structure of the data and the rules found for each class, especially to track dichotomies and inequality. The results reached by our proposal on the involved datasets have been very promising when used in classification tasks and compared with other methods

A Longitudinal Study of Mammograms Utilizing the Automated Wavelet Transform Modulus Maxima Method

Breast cancer is a disease which predominatly affects women. About 1 in 8 women are diagnosed with breast cancer during their lifetime. Early detection is key to increasing the survival rate of breast cancer patients since the longer the tumor goes undetected, the more deadly it can become. The modern approach for diagnosing breast cancer relies on a combination of self-breast exams and mammography to detect the formation of tumors. However, this approach only accounts for tumors which are either detectable by touch or are large enough to be observed during a screening mammogram. For some individuals, by the time a tumor is detected, it has already progressed to a deadly stage. Unlike previous research, this paper focuses on the predetection of tumorous tissue. This novel approach sets out to examine changes in the breast microenvironment instead of locating and identifying tumors. The purpose of this paper is to explore whether it is possible to discover changes in the breast tissue microenvironment which later develop into breast cancer. We hypothesized that changes in the breast tissue would be detected by analyzing mammograms from the years prior to the discovery of tumorous tissue by a radiologist. We analyzed a set of time-series digital mammograms corresponding to 26 longitudinal cancer cases, obtained through a collaboration with Eastern Maine Medical Center (EMMC) in Bangor, Maine. We automated the Wavelet Transform Modulus Maxima (WTMM) method, a mathematical formalism that we used to perform a multifractal analysis. In particular, this automated WTMM (AWTMM) was used to calculate the Hurst exponent, a metric that is correlated with breast tissue density. The AWTMM allowed us to see with greater detail the changes in mammogram tissue, specifically concerning breast density. The results suggest that signs of malignancy can be observed as early as two years before standard radiological procedures. In this research, we identify a set of variables that show significance when classifying precancerous tissue