Shape-Based Breast Lesion Classification Using Digital Tomosynthesis Images: The Role of Explainable Artificial Intelligence

Abstract

Computer-aided diagnosis (CAD) systems can help radiologists in numerous medical tasks including classification and staging of the various diseases. The 3D tomosynthesis imaging technique adds value to the CAD systems in diagnosis and classification of the breast lesions. Several convolutional neural network (CNN) architectures have been proposed to classify the lesion shapes to the respective classes using a similar imaging method. However, not only is the black box nature of these CNN models questionable in the healthcare domain, but so is the morphological-based cancer classification, concerning the clinicians. As a result, this study proposes both a mathematically and visually explainable deep-learning-driven multiclass shape-based classification framework for the tomosynthesis breast lesion images. In this study, authors exploit eight pretrained CNN architectures for the classification task on the previously extracted regions of interests images containing the lesions. Additionally, the study also unleashes the black box nature of the deep learning models using two well-known perceptive explainable artificial intelligence (XAI) algorithms including Grad-CAM and LIME. Moreover, two mathematical-structure-based interpretability techniques, i.e., t-SNE and UMAP, are employed to investigate the pretrained models’ behavior towards multiclass feature clustering. The experimental results of the classification task validate the applicability of the proposed framework by yielding the mean area under the curve of 98.2%. The explanability study validates the applicability of all employed methods, mainly emphasizing the pros and cons of both Grad-CAM and LIME methods that can provide useful insights towards explainable CAD systems.

NDG-CAM: Nuclei Detection in Histopathology Images with Semantic Segmentation Networks and Grad-CAM

“…A CNN is often seen as a black box, or rather, as a model with parameters W that, given an image of input X , through a function , is able to map to the related output y . XAI techniques have been designed in order to unveil the underlying mechanisms involved in the processing stages of deep neural networks, and are recently gaining a lot of attention in medical imaging and clinical decision support systems [ 32 , 33 , 34 , 35 ].…”

“…During the training phase, even if we are capable of achieving high performance according to the considered metrics, we do not know which image features are more determinant for the network to make its choices. One of the ways to visually solve this problem is Grad-CAM [ 35 ].…”

A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net

“…The semantic segmentation of the prostate gland from MRI can be efficiently met via DL techniques, as fully convolutional neural networks [ 24 ]. Semantic segmentation, which poses the basis for subsequent classification and characterization tasks [ 25 , 26 ], is essential in numerous clinical applications including artificial intelligence in diagnostic support systems, therapy planning, intraoperative assistance, and monitoring of tumor growth. It is a computer vision task that can be computed with DL algorithms and consists of labeling each pixel of an input image, without recognizing the different instances of objects [ 27 , 28 ]; it is possible to see semantic segmentation as a problem of conversion from image to image, where the input image is the original image and each pixel intensity value of the output image indicates the relation of that pixel to the associated class [ 29 ].…”

Time-Series Clustering of Single-Cell Trajectories in Collective Cell Migration

“…To visualize the similarity of the cell trajectories, we embedded each cell trajectory into a two-dimensional space based on the distance matrix . We employed t-SNE [ 24 , 29 , 30 , 31 ] and UMAP [ 25 ] as the embedding methods using Python (scikit-learn 1.1.2; umap-learn 0.5.3). By applying these two methods, we obtain a set of cell trajectories in th time period represented in a two-dimensional space: where is the th cell trajectory of the th time period in two-dimensional space.…”