Added benefits of computer-assisted analysis of Hematoxylin-Eosin stained breast histopathological digital slides

This thesis aims at determining if computer-assisted analysis can be used to better understand pathologists’ perception of mitotic figures on Hematoxylin-Eosin (HE) stained breast histopathological digital slides. It also explores the feasibility of reproducible histologic nuclear atypia scoring by incorporating computer-assisted analysis to cytological scores given by a pathologist. In addition, this thesis investigates the possibility of computer-assisted diagnosis for categorizing HE breast images into different subtypes of cancer or benign masses. In the first study, a data set of 453 mitoses and 265 miscounted non-mitoses within breast cancer digital slides were considered. Different features were extracted from the objects in different channels of eight colour spaces. The findings from the first research study suggested that computer-aided image analysis can provide a better understanding of image-related features related to discrepancies among pathologists in recognition of mitoses. Two tasks done routinely by the pathologists are making diagnosis and grading the breast cancer. In the second study, a new tool for reproducible nuclear atypia scoring in breast cancer histological images was proposed. The third study proposed and tested MuDeRN (MUlti-category classification of breast histopathological image using DEep Residual Networks), which is a framework for classifying hematoxylin-eosin stained breast digital slides either as benign or cancer, and then categorizing cancer and benign cases into four different subtypes each. The studies indicated that computer-assisted analysis can aid in both nuclear grading (COMPASS) and breast cancer diagnosis (MuDeRN). The results could be used to improve current status of breast cancer prognosis estimation through reducing the inter-pathologist disagreement in counting mitotic figures and reproducible nuclear grading. It can also improve providing a second opinion to the pathologist for making a diagnosis

Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification

This paper explores the problem of breast tissue classification of microscopy images. Based on the predominant cancer type the goal is to classify images into four categories of normal, benign, in situ carcinoma, and invasive carcinoma. Given a suitable training dataset, we utilize deep learning techniques to address the classification problem. Due to the large size of each image in the training dataset, we propose a patch-based technique which consists of two consecutive convolutional neural networks. The first "patch-wise" network acts as an auto-encoder that extracts the most salient features of image patches while the second "image-wise" network performs classification of the whole image. The first network is pre-trained and aimed at extracting local information while the second network obtains global information of an input image. We trained the networks using the ICIAR 2018 grand challenge on BreAst Cancer Histology (BACH) dataset. The proposed method yields 95 % accuracy on the validation set compared to previously reported 77 % accuracy rates in the literature. Our code is publicly available at https://github.com/ImagingLab/ICIAR2018Comment: 10 pages, 5 figures, ICIAR 2018 conferenc

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

Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions

Objective. This study investigates the potential of an artificial intelligence (AI) methodology, the radial basis function (RBF) artificial neural network (ANN), in the evaluation of thyroid lesions. Study Design. The study was performed on 447 patients who had both cytological and histological evaluation in agreement. Cytological specimens were prepared using liquid-based cytology, and the histological result was based on subsequent surgical samples. Each specimen was digitized; on these images, nuclear morphology features were measured by the use of an image analysis system. The extracted measurements (41,324 nuclei) were separated into two sets: the training set that was used to create the RBF ANN and the test set that was used to evaluate the RBF performance. The system aimed to predict the histological status as benign or malignant. Results. The RBF ANN obtained in the training set has sensitivity 82.5%, specificity 94.6%, and overall accuracy 90.3%, while in the test set, these indices were 81.4%, 90.0%, and 86.9%, respectively. Algorithm was used to classify patients on the basis of the RBF ANN, the overall sensitivity was 95.0%, the specificity was 95.5%, and no statistically significant difference was observed. Conclusion. AI techniques and especially ANNs, only in the recent years, have been studied extensively. The proposed approach is promising to avoid misdiagnoses and assists the everyday practice of the cytopathology. The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images

Computerized cancer malignancy grading of fine needle aspirates

According to the World Health Organization, breast cancer is a leading cause of death among middle-aged women. Precise diagnosis and correct treatment significantly reduces the high number of deaths caused by breast cancer. Being successful in the treatment strictly relies on the diagnosis. Specifically, the accuracy of the diagnosis and the stage at which a cancer was diagnosed. Precise and early diagnosis has a major impact on the survival rate, which indicates how many patients will live after the treatment. For many years researchers in medical and computer science fields have been working together to find the approach for precise diagnosis. For this thesis, precise diagnosis means finding a cancer at as early a stage as possible by developing new computer aided diagnostic tools. These tools differ depending on the type of cancer and the type of the examination that is used for diagnosis. This work concentrates on cytological images of breast cancer that are produced during fine needle aspiration biopsy examination. This kind of examination allows pathologists to estimate the malignancy of the cancer with very high accuracy. Malignancy estimation is very important when assessing a patients survival rate and the type of treatment. To achieve precise malignancy estimation, a classification framework is presented. This framework is able to classify breast cancer malignancy into two malignancy classes and is based on features calculated according to the Bloom-Richardson grading scheme. This scheme is commonly used by pathologists when grading breast cancer tissue. In Bloom-Richardson scheme two types of features are assessed depending on the magnification. Low magnification images are used for examining the dispersion of the cells in the image while the high magnification images are used for precise analysis of the cells' nuclear features. In this thesis, different types of segmentation algorithms were compared to estimate the algorithm that allows for relatively fast and accurate nuclear segmentation. Based on that segmentation a set of 34 features was extracted for further malignancy classification. For classification purposes 6 different classifiers were compared. From all of the tests a set of the best preforming features were chosen. The presented system is able to classify images of fine needle aspiration biopsy slides with high accurac