Breast cancer mass detection in dce-mri using deep-learning features followed by discrimination of infiltrative vs. in situ carcinoma through a machine-learning approach

Breast cancer is the leading cause of cancer deaths worldwide in women. This aggressive tumor can be categorized into two main groups-in situ and infiltrative, with the latter being the most common malignant lesions. The current use of magnetic resonance imaging (MRI) was shown to provide the highest sensitivity in the detection and discrimination between benign vs. malignant lesions, when interpreted by expert radiologists. In this article, we present the prototype of a computer-aided detection/diagnosis (CAD) system that could provide valuable assistance to radiologists for discrimination between in situ and infiltrating tumors. The system consists of two main processing levels-(1) localization of possibly tumoral regions of interest (ROIs) through an iterative procedure based on intensity values (ROI Hunter), followed by a deep-feature extraction and classification method for false-positive rejection; and (2) characterization of the selected ROIs and discrimination between in situ and invasive tumor, consisting of Radiomics feature extraction and classification through a machine-learning algorithm. The CAD system was developed and evaluated using a DCE-MRI image database, containing at least one confirmed mass per image, as diagnosed by an expert radiologist. When evaluating the accuracy of the ROI Hunter procedure with respect to the radiologist-drawn boundaries, sensitivity to mass detection was found to be 75%. The AUC of the ROC curve for discrimination between in situ and infiltrative tumors was 0.70

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

Computer-aided Cytological Grading Systems for Fine Needle Aspiration Biopsies of Breast Cancer

According to the American Cancer Society, breast cancer is the world's most commonly diagnosed and deadliest form of cancer in women. A major determinant of the survival rate in breast cancer patients are the accuracy and speed of the malignancy grade determination. This thesis considers the classification problem related to determining the grade of a malignant tumor accurately and efficiently. A Fine Needle Aspiration (FNA) biopsy is a key mechanism for breast cancer diagnosis as well as for assigning grades to malignant cases. Carrying out a manual examination of FNA demands substantial work from the pathologist which may result in delays, human errors, and consequently lead to misclassified grades. In this context, the most common grading system for microscopic imaging for breast cancer is the Bloom and Richardson (BR) histological grading system which is based on the evaluation of tissues and cells. BR is not directly applicable to FNA biopsy slides due to distortion of tissue and even cell structures on the cytological slides. Therefore, in this thesis, to grade FNA images of breast cancer, instead of the BR grading scheme, six known cytological grading schemes, three newly proposed cytological grading schemes, and five grading systems based on convolutional neural networks were proposed to automatically determine the malignancy grade of breast cancer. First, considering traditional Machine Learning methods, six cytological grading systems (CA-CGSs) based on six cytological schemes used by pathologists for FNA biopsies of breast cancer were proposed to grade tumors. Each system was built using the cytological criteria as proposed in the original CGSs. The six considered cytological grading schemes in this thesis were Fisher's modification of Black's nuclear grading, Mouriquand's grading, Robinson's grading, Taniguchi et al's, Khan et al's and Howell's modification in mitosis count criteria. To fulfill this task, different sets of handcrafted features using customized image processing algorithms were extracted for classification purpose. The proposed systems were able efficiently to classify FNA slides into G2 (moderately malignant) or G3 (highly malignant) cases using traditional machine learning algorithms. Additionally, three new cytological grading systems were proposed by augmenting three of the original CGSs by adding the low magnification features. However, the systems were not sensitive enough with regards to G3 cases due to the low number of available data samples. Therefore, a data balancing was performed to improve the sensitivity for G3 cases. Consequently, in the second objective of this work, data sampling and RUSBoost methods were applied to the datasets to adjust the class distribution and boost the sensitivity performance of the proposed systems. This enabled a sensitivity improvement of up to 30% which highlights the significance of class balancing in the task of malignancy grading of breast cancer. Additionally, due to the considerable time and efforts required for handcrafted features-based cytological grading systems in order to achieve efficient feature engineering results, a deep learning (DL) approach was proposed to avoid the aforementioned challenges without compromising the grading accuracy. Thus, in this thesis, five different pre-trained convolutional neural network (CNN) models, namely GoogleNet Inception-v3, AlexNet, ResNet18, ResNet50, and ResNet101, combined with different techniques to deal with unbalanced data, were used to develop automated computer-aided cytological malignancy grading systems (CNN-CMGSs). According to the obtained results, the proposed CNN-CMGS based on GoogleNet Inception-v3 combined with the oversampling method provides the best accuracy performance for the problem at hand. The results demonstrated that the proposed CGSs are highly correlated since they share some of the cytological criteria. Further, the overall accuracy of the CGSs is roughly the same and overall, the handcrafted features-based CGSs performed best even in the absence of class distribution rebalancing. Overall, for case classification, the best results were obtained for computer-aided CGSs based on the modified Khan et al.’s and Robinson’s schemes with accuracies of 97.77% and 97.28%, respectively. Meanwhile, for patient classification, the overall best results were obtained for computer-aided CGSs based on the modified Khan et al.’s and modified Fisher's schemes with accuracies of 96.50% and 95.71%, respectively. These results surpass previously reported results in the literature for computer-aided CGS based on BR histologic grading. Moreover, in clinical practice, Robinson’s typically has the best diagnostic accuracy with the highest reported experimental accuracy rate of 90%. Thus, the obtained results demonstrate that computer-aided breast cancer cytological grading systems using FNA can potentially achieve accuracy rates comparable to the more invasive histopathological BR-method

IMAGE UNDERSTANDING OF MOLAR PREGNANCY BASED ON ANOMALIES DETECTION

Cancer occurs when normal cells grow and multiply without normal control. As the cells multiply, they form an area of abnormal cells, known as a tumour. Many tumours exhibit abnormal chromosomal segregation at cell division. These anomalies play an important role in detecting molar pregnancy cancer. Molar pregnancy, also known as hydatidiform mole, can be categorised into partial (PHM) and complete (CHM) mole, persistent gestational trophoblastic and choriocarcinoma. Hydatidiform moles are most commonly found in women under the age of 17 or over the age of 35. Hydatidiform moles can be detected by morphological and histopathological examination. Even experienced pathologists cannot easily classify between complete and partial hydatidiform moles. However, the distinction between complete and partial hydatidiform moles is important in order to recommend the appropriate treatment method. Therefore, research into molar pregnancy image analysis and understanding is critical. The hypothesis of this research project is that an anomaly detection approach to analyse molar pregnancy images can improve image analysis and classification of normal PHM and CHM villi. The primary aim of this research project is to develop a novel method, based on anomaly detection, to identify and classify anomalous villi in molar pregnancy stained images. The novel method is developed to simulate expert pathologists’ approach in diagnosis of anomalous villi. The knowledge and heuristics elicited from two expert pathologists are combined with the morphological domain knowledge of molar pregnancy, to develop a heuristic multi-neural network architecture designed to classify the villi into their appropriated anomalous types. This study confirmed that a single feature cannot give enough discriminative power for villi classification. Whereas expert pathologists consider the size and shape before textural features, this thesis demonstrated that the textural feature has a higher discriminative power than size and shape. The first heuristic-based multi-neural network, which was based on 15 elicited features, achieved an improved average accuracy of 81.2%, compared to the traditional multi-layer perceptron (80.5%); however, the recall of CHM villi class was still low (64.3%). Two further textural features, which were elicited and added to the second heuristic-based multi-neural network, have improved the average accuracy from 81.2% to 86.1% and the recall of CHM villi class from 64.3% to 73.5%. The precision of the multi-neural network II has also increased from 82.7% to 89.5% for normal villi class, from 81.3% to 84.7% for PHM villi class and from 80.8% to 86% for CHM villi class. To support pathologists to visualise the results of the segmentation, a software tool, Hydatidiform Mole Analysis Tool (HYMAT), was developed compiling the morphological and pathological data for each villus analysis