Traditional pathology approaches have played an integral role in the delivery of diagnosis,
semi-quantitative or qualitative assessment of protein expression, and classification of …

Breast Cancer (BC) is the most commonly diagnosed cancer and second leading cause of
mortality among women. About 1 in 8 US women (about 13%) will develop invasive BC …

Deep Learning (DL) methods are powerful analytical tools for microscopy and can
outperform conventional image processing pipelines. Despite the enthusiasm and …

Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers.
However, the growing number of these complex biomarkers tends to increase the cost and …

This paper aims to review Artificial neural networks, Multi-Layer Perceptron Neural network
(MLP) and Convolutional Neural network (CNN) employed to detect breast malignancies for …

The number of publications on deep learning for cancer diagnostics is rapidly increasing,
and systems are frequently claimed to perform comparable with or better than clinicians …

In the past decade, advances in precision oncology have resulted in an increased demand
for predictive assays that enable the selection and stratification of patients for treatment. The …

Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the
problem of explainability is as old as AI itself and classic AI represented comprehensible …

Simple Summary In this research, we addressed the challenging task of brain tumor
detection in MRI scans using a large collection of brain tumor images. We demonstrated that …

Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …