In cancer diagnostics, a considerable amount of data is acquired during routine work-up.
Recently, machine learning has been used to build classifiers that are tasked with cancer …

Breast tumor segmentation and recognition from mammograms play a key role in healthcare
and treatment services. As different tumors in mammography have dissimilar densities …

Breast cancer is cancer that develops from the breast tissue and has been recognized as
one of the most dangerous and deadly diseases that is the second leading cause of cancer …

The COVID‐19 pandemic is a global, national, and local public health concern which has
caused a significant outbreak in all countries and regions for both males and females …

The incidence of thyroid disease has gradually increased in recent years. Conventional
ultrasound is one of the most critical thyroid imaging methods, but it still has certain …

Computer aided diagnosis for mammogram images have seen positive results through the
usage of deep learning architectures. However, limited sample sizes for the target datasets …

Purpose: We introduce and evaluate deep learning methods for weakly supervised
segmentation of tumor lesions in whole-body fluorodeoxyglucose-positron emission …

Clinicians are the use-case experts who are best suited to drive AI projects tackling patient-
relevant outcome measures. Taken together, open datasets, efficient labeling techniques …

Identifying calcifications in mammograms is crucial for early breast cancer detection, and
semi-supervised learning, which utilizes a small dataset for supervised learning combined …

Objective. To develop and evaluate a multi-path synergic fusion (MSF) deep neural network
model for breast mass classification using digital breast tomosynthesis (DBT). Methods. We …