Machine Learning Methods for Breast Cancer Diagnostic

Abstract

This chapter discusses radio-pathological correlation with recent imaging advances such as machine learning (ML) with the use of technical methods such as mammography and histopathology. Although criteria for diagnostic categories for radiology and pathology are well established, manual detection and grading, respectively, are tedious and subjective processes and thus suffer from inter-observer and intra-observer variations. Two most popular techniques that use ML, computer aided detection (CADe) and computer aided diagnosis (CADx), are presented. CADe is a rejection model based on SVM algorithm which is used to reduce the False Positive (FP) of the output of the Chan-Vese segmentation algorithm that was initialized by the marker controller watershed (MCWS) algorithm. CADx method applies the ensemble framework, consisting of four-base SVM (RBF) classifiers, where each base classifier is a specialist and is trained to use the selected features of a particular tissue component. In general, both proposed methods offer alternative decision-making ability and are able to assist the medical expert in giving second opinion on more precise nodule detection. Hence, it reduces FP rate that causes over segmentation and improves the performance for detection and diagnosis of the breast cancer and is able to create a platform that integrates diagnostic reporting system.

Application of Artificial Intelligence in the Mammographic Detection of Breast Cancer in Saudi Arabian Women

“…Owing to the increased interest in AI in radiology applications, various modern deep learning-based algorithms have been created and applied to digital mammography. Preliminary research has shown that the use of AI systems to provide concurrent mammographic interpretations can increase radiological efficiency in terms of time, sensitivity, and specificity [22,32]. AI systems based on CAD neural network algorithms are useful in the detection of breast lesions and in reducing the false-positive rate.…”

“…AI techniques for cancer imaging in health care have recently been investigated, developed, and evaluated as support technologies for disease detection, prognosis, and clinical decision making [8,11,20]. This has enhanced the accuracy and efficiency of breast cancer detection, assisting radiologists with breast cancer screenings and reducing the workload of second readers [14,17,18,[21][22][23]. Current evidence has highlighted that the use of AI-based tools has improved radiologists' breast cancer detection by mammography without the additional reading time required [17,24].…”

Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology

“…As a summary, the collective endeavors in breast cancer histopathology image classification underscore the significance of ensemble strategies [19][20][21][22], direct application of CNN architectures [23][24][25], and the fusion of transfer learning with innovative model designs [26][27][28]. Inspired by these pioneering works, our study aims to build upon existing methodologies and enhance performance on the aforementioned datasets through innovative techniques and meticulous experimentation.…”

Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology

“…As a summary, the collective endeavors in breast cancer histopathology image classification underscore the significance of ensemble strategies [ 25 , 26 , 27 , 28 ], direct application of CNN architectures [ 29 , 30 , 31 ], and the fusion of transfer learning with innovative model designs [ 32 , 33 , 34 ]. Inspired by these pioneering works, our study aimed to build upon existing methodologies and enhance performance on the aforementioned datasets through innovative techniques and meticulous experimentation.…”