Microcalcification Detection Applying Artificial Neural Networks and Mathematical Morphology in Digital Mammograms

Breast cancer is one of the leading causes to women mortality in the world and early detection is an important means to reduce the mortality rate. The presence of microcalcifications clusters has been considered as a very important indicator of malignant types of breast cancer and its detection is important to prevent and treat the disease. This paper presents an alternative and effective approach in order to detect microcalcifications clusters in digitized mammograms based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. A k-means algorithm is used to cluster the data based on the features vectors and finally an artificial neural network-based classifier is applied and the classification performance is evaluated by a ROC curve. Experimental results indicate that the percentage of correct classification was 99.72%, obtaining 100% true positive (sensitivity) and 99.67% false positive (specificity), with the best classifier proposed. In case of the best classifier, we obtained a performance evaluation of classification of Az = 0.987

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

Data mining in biomedicine : current applications and further directions for research

Author name used in this manuscript: S. K. KwokAuthor name used in this manuscript: A. H. C. Tsang2009-2010 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

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

Mamogram görüntülerinden makine öğrenmesi yöntemleri ile meme kanseri teşhisi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Meme kanseri son yıllarda kanser türleri arasında en çok yaygınlık gösteren kanser türüdür. Meme kanserinin teşhisi ve tedavisinde mamografi olarak bilinen X-Ray görüntüleme yöntemi yaygın bir şekilde kullanılmaktadır. Mamografi cihazları ile elde edilen mamogram görüntüleri radyoloji uzmanları tarafından incelenir, yorumlanır ve hasta ile ilgili rapor yazılır. Mamogram görüntülerinde uzmanlar öncelikle kitle tespit etmeye ve mikrokireçlenme(MC, Microcalcification) tespit etmeye çalışırlar. MC tespiti kitle tespitine göre gözden kaçırılma riski daha fazla olan bir durumdur. Yapılan araştırmalarda radyologların MC vakalarını tespit etmekte zorlandıklarını ve yüzde yetmişlik bir doğrulukla çalıştıkları ortaya koyulmuştur. Son yıllarda meme kanseri teşhisi alanında bilgisayar destekli tespit sistemleri geliştirilmeye başlanmıştır. Araştırmacılar mamogram görüntüleri üzerinde kitle tespiti yapan veya MC tespiti yapan yöntemler yaklaşımlar ve algoritmalar geliştirmektedir. Bu çalışmada MC bölgelerinin tespitini yapmak için makine öğrenmesi yöntemi kullanılarak bir çalışma yapılmıştır. Yapılan çalışmada gri seviye eş oluşum matrisi temelli doku analizi (GLCM, Gray Level Cooccurrance Matrix), dalgacık dönüşümü temelli ayrıştırma, iki boyutlu eşit genişlikli ayrıştırma (EWD2) ve çoklu pencere temelli istatistiki analiz (MWBSA) kullanılarak farklı özellik çıkartım yöntemleri ile MC desenlerinin karakteristik özellikleri sayısal yöntemlerle analiz edilmiş olup çok katmanlı ileri beslemeli yapay sinir ağı (MLPNN, Multiple Layer Percepteron Neural Network) olarak bilinen sınıflandırıcı ve destek vektör makinesi (SVM, Support Vector Machine) kullanılarak bir makine öğrenmesi yaklaşımı geliştirilmiştir. Çalışma sonuçlarının geçerliliği, tıbbi karar verme sürecinde bir testin ayırt ediciliğini belirlemek amacıyla kullanılan yöntemlerden biri olan Alıcı İşlem Karakteristikleri Eğrisi (ROC, Receiver Operating Characteristic) yöntemi kullanılarak yapılmıştır. Duyarlılık ve özgüllük testi olarak da bilinen bu test neticesinde aday mikrokireçlenme tespit aşamasında MLPNN sınıflandırıcı kullanılarak en iyi sonuç MWBSA yöntemi ile elde edilmiştir. SVM sınıflandırıcı kullanılarak en iyi sonuç ise EWD2 ve GLCM yöntemleri kullanılarak elde edilmiştir. Aday mikrokireçlenme bölgelerinin sınıflandırılması olan ikinci aşamada ise MLPNN sınıflandırıcı kullanılarak en iyi sonuç EWD2 yöntemi ve GLCM yöntemi kullanılarak elde edilirken SVM sınıflandırıcı kullanılarak yapılan deneylerde en iyi sonuç dalgacık dönüşümü yöntemi kullanılarak elde ediliştir. Çalışmanın sonunda MATLAB yazılım geliştirme ortamı kullanılarak grafik arayüze sahip BCDS ismi verilen MC temelli meme kanseri teşhis yazılımı geliştirilmiştir. Geliştirilen bu yazılım gelecekte üzerine yeni özellik çıkartım yöntemleri ve yeni sınıflandırıcı modelleri eklenebilecek şekilde dinamik bir yapıya sahiptir.Breast cancer is the most common cancer type among other cancer types in recent years. X-ray imaging method, known as mammography for diagnosis and treatment of breast cancer, is widely used. The mammogram images, produced by mammography devices, are examined, interpreted, and a report about the patient is written by radiologists. Radiologists first try to catch masses and microcalcifications in mammogram images. Detection of microcalcification (MC) is a more difficult process than mass detection. Research has shown that radiologists have difficulty detecting microcalcification and they work with seventy percent accuracy. In recent years several computer aided detection systems have been developed on breast cancer diagnosis. Researchers have been developing methods, approaches and algorithms catching masses and MC in mammogram images. In this study machine learning method was used for detection of microcalcification problem. In the current study, the characteristic features of MC patterns were analyzed by using quantitative methods such as gray level co-occurrence matrix based texture analysis (GLCM), wavelet-based parsing, two-dimensional equal width separation (EWD2), and multi-window based statistical analysis (MWBSA), and a machine learning approach was developed by employing a classifier and support vector machine (CSM) known as multi-layer percepteron neural network (MLPNN). The validity of the study findings was performed using the Receiver Operating Characteristic (ROC) method, which is used for determining the distinctiveness of a test during a medical decision making process. As a result of this test, also known as sensitivity and specificity test, the best result was obtained with MWBSA method using MLFFNN classifier during microcalcification diagnosis process. The best result for CSM classifier was obtained using EWD2 and GLCM methods. At the second stage, which is the classification of candidate microcalcifications, the best values for MLFFNN classifier were obtained using EWD2 and GLCM methods, whereas the best result in experiments employing CSM classifier was obtained using wavelet method. At the end of the study, MC based breast cancer detection system called BCDS with a GUI was developed using MATLAB. The developed software is a dynamic and well suited structure into which new classifier models and extraction methods can be integrated in the future

Hypothyroidism

Hypothyroidism is an endocrine disorder commonly caused by Hashimoto’s disease. Nowadays, autoimmune diseases appear to be on the rise. As such, there is renewed interest in hypothyroidism. This book presents a comprehensive overview of the disorder with chapters on etiology and pathogenesis, precision medicine tools for detection, diagnosis and treatment, the morphology of the thyroid gland, the effect of hypothyroidism on various organ systems, and much more

Multimodal Data Fusion and Quantitative Analysis for Medical Applications

Medical big data is not only enormous in its size, but also heterogeneous and complex in its data structure, which makes conventional systems or algorithms difficult to process. These heterogeneous medical data include imaging data (e.g., Positron Emission Tomography (PET), Computerized Tomography (CT), Magnetic Resonance Imaging (MRI)), and non-imaging data (e.g., laboratory biomarkers, electronic medical records, and hand-written doctor notes). Multimodal data fusion is an emerging vital field to address this urgent challenge, aiming to process and analyze the complex, diverse and heterogeneous multimodal data. The fusion algorithms bring great potential in medical data analysis, by 1) taking advantage of complementary information from different sources (such as functional-structural complementarity of PET/CT images) and 2) exploiting consensus information that reflects the intrinsic essence (such as the genetic essence underlying medical imaging and clinical symptoms). Thus, multimodal data fusion benefits a wide range of quantitative medical applications, including personalized patient care, more optimal medical operation plan, and preventive public health. Though there has been extensive research on computational approaches for multimodal fusion, there are three major challenges of multimodal data fusion in quantitative medical applications, which are summarized as feature-level fusion, information-level fusion and knowledge-level fusion: • Feature-level fusion. The first challenge is to mine multimodal biomarkers from high-dimensional small-sample multimodal medical datasets, which hinders the effective discovery of informative multimodal biomarkers. Specifically, efficient dimension reduction algorithms are required to alleviate "curse of dimensionality" problem and address the criteria for discovering interpretable, relevant, non-redundant and generalizable multimodal biomarkers. • Information-level fusion. The second challenge is to exploit and interpret inter-modal and intra-modal information for precise clinical decisions. Although radiomics and multi-branch deep learning have been used for implicit information fusion guided with supervision of the labels, there is a lack of methods to explicitly explore inter-modal relationships in medical applications. Unsupervised multimodal learning is able to mine inter-modal relationship as well as reduce the usage of labor-intensive data and explore potential undiscovered biomarkers; however, mining discriminative information without label supervision is an upcoming challenge. Furthermore, the interpretation of complex non-linear cross-modal associations, especially in deep multimodal learning, is another critical challenge in information-level fusion, which hinders the exploration of multimodal interaction in disease mechanism. • Knowledge-level fusion. The third challenge is quantitative knowledge distillation from multi-focus regions on medical imaging. Although characterizing imaging features from single lesions using either feature engineering or deep learning methods have been investigated in recent years, both methods neglect the importance of inter-region spatial relationships. Thus, a topological profiling tool for multi-focus regions is in high demand, which is yet missing in current feature engineering and deep learning methods. Furthermore, incorporating domain knowledge with distilled knowledge from multi-focus regions is another challenge in knowledge-level fusion. To address the three challenges in multimodal data fusion, this thesis provides a multi-level fusion framework for multimodal biomarker mining, multimodal deep learning, and knowledge distillation from multi-focus regions. Specifically, our major contributions in this thesis include: • To address the challenges in feature-level fusion, we propose an Integrative Multimodal Biomarker Mining framework to select interpretable, relevant, non-redundant and generalizable multimodal biomarkers from high-dimensional small-sample imaging and non-imaging data for diagnostic and prognostic applications. The feature selection criteria including representativeness, robustness, discriminability, and non-redundancy are exploited by consensus clustering, Wilcoxon filter, sequential forward selection, and correlation analysis, respectively. SHapley Additive exPlanations (SHAP) method and nomogram are employed to further enhance feature interpretability in machine learning models. • To address the challenges in information-level fusion, we propose an Interpretable Deep Correlational Fusion framework, based on canonical correlation analysis (CCA) for 1) cohesive multimodal fusion of medical imaging and non-imaging data, and 2) interpretation of complex non-linear cross-modal associations. Specifically, two novel loss functions are proposed to optimize the discovery of informative multimodal representations in both supervised and unsupervised deep learning, by jointly learning inter-modal consensus and intra-modal discriminative information. An interpretation module is proposed to decipher the complex non-linear cross-modal association by leveraging interpretation methods in both deep learning and multimodal consensus learning. • To address the challenges in knowledge-level fusion, we proposed a Dynamic Topological Analysis framework, based on persistent homology, for knowledge distillation from inter-connected multi-focus regions in medical imaging and incorporation of domain knowledge. Different from conventional feature engineering and deep learning, our DTA framework is able to explicitly quantify inter-region topological relationships, including global-level geometric structure and community-level clusters. K-simplex Community Graph is proposed to construct the dynamic community graph for representing community-level multi-scale graph structure. The constructed dynamic graph is subsequently tracked with a novel Decomposed Persistence algorithm. Domain knowledge is incorporated into the Adaptive Community Profile, summarizing the tracked multi-scale community topology with additional customizable clinically important factors

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

06. 2010 IMSAloquium Student Investigation Showcase

https://digitalcommons.imsa.edu/class_of_2010/1004/thumbnail.jp