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

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

A new deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimisation of the proposed model

Deep learning algorithms have yielded remarkable results in medical diagnosis and image analysis, besides their contribution to improvements in a number of fields such as drug discovery, time-series modelling and optimisation methods. With regard to the analysis of histopathologic breast cancer images, the similarity of those images and the presence of healthy and tumourous tissues in different areas complicate the detection and classification of tumours on whole slide images. An accurate diagnosis in a short time is a need for full treatment in breast cancer. A successful classification on breast cancer histopathological images will overcome the burden on the pathologist and reduce the subjectivity of diagnosis. In this study, we propose a deep convolutional neural network model. The model uses various algorithms (i.e., stochastic gradient descent, Nesterov accelerated gradient, adaptive gradient, RMSprop, AdaDelta and Adam) to compute the initial weight of the network and update the model parameters for faster backpropagation learning. In order to train the model with less hardware in a short time, we used the parallel computing architecture with Cuda-enabled graphics processing unit. The results indicate that the deep convolutional neural network model is an effective classification model with a high performance up to 99.05% accuracy value. © 2020, Springer Science+Business Media, LLC, part of Springer Nature

Can high-frequency ultrasound predict metastatic lymph nodes in patients with invasive breast cancer?

Aim To determine whether high-frequency ultrasound can predict the presence of metastatic axillary lymph nodes, with a high specificity and positive predictive value, in patients with invasive breast cancer. The clinical aim is to identify patients with axillary disease requiring surgery who would not normally, on clinical grounds, have an axillary dissection, so potentially improving outcome and survival rates. Materials and methods The ipsilateral and contralateral axillae of 42 consecutive patients with invasive breast cancer were scanned prior to treatment using a B-mode frequency of 13 MHz and a Power Doppler frequency of 7 MHz. The presence or absence of an echogenic centre for each lymph node detected was recorded, and measurements were also taken to determine the L/S ratio and the widest and narrowest part of the cortex. Power Doppler was also used to determine vascularity. The contralateral axilla was used as a control for each patient. Results In this study of patients with invasive breast cancer, ipsilateral lymph nodes with a cortical bulge ≥3 mm and/or at least two lymph nodes with absent echogenic centres indicated the presence of metastatic axillary lymph nodes (10 patients). The sensitivity and specificity were 52.6% and 100%, respectively, positive and negative predictive values were 100% and 71.9%, respectively, the P value was 0.001 and the Kappa score was 0.55.\ud Conclusion This would indicate that high-frequency ultrasound can be used to accurately predict metastatic lymph nodes in a proportion of patients with invasive breast cancer, which may alter patient management

Future perspectives of digital pathology

Technological advances have enabled innovative solutions to be achieved in pathology based on digital imaging, now superseding those of conventional microscopy. Digital pathology has been defined as ‘virtual microscopy’ and depends on computer-generated digital imaging of microscope slides (WSI — whole slide imaging) which are in turn created, reviewed, managed, shared, analysed and interpreted. Such WSI systems and digital consulting platforms are now used for teaching, scientific research, telepathology / teleconsultation and diagnostics. They also permit easy and interactive sharing of WSI that can be integrated into other medical information systems. The software for automated image analysis and computer aided diagnosis can thereby make highly accurate diagnoses and help standardise study findings. Despite the technique’s many advantages, its noted drawbacks include high equipment and software costs, image quality issues of standardisation and most importantly, that pathologists are reluctant to use it routinely for making diagnoses