We assessed the feasibility of a data-driven imaging biomarker based on weakly supervised
learning (DIB; an imaging biomarker derived from large-scale medical image data with deep …

The rapid development of deep learning, a family of machine learning techniques, has
spurred much interest in its application to medical imaging problems. Here, we develop a …

Breast mass detection is a challenging task in mammogram, since mass is usually
embedded and surrounded by various normal tissues with similar density. Recently, deep …

Purpose: False positives in digital mammography screening lead to high recall rates,
resulting in unnecessary medical procedures to patients and health care costs. This study …

Rationale and Objectives To evaluate a weakly supervised deep learning approach to
breast Magnetic Resonance Imaging (MRI) assessment without pixel level segmentation in …

Objectives The objective of this study was to develop and validate a state-of-the-art, deep
learning (DL)-based model for detecting breast cancers on mammography. Methods …

Breast cancer is the second deadliest cancer among women. Mammography is an important
method for physicians to diagnose breast cancer. The main purpose of this study is to use …

In order to establish a new personalized breast cancer screening paradigm, it is critically
important to accurately predict the short-term risk of a woman having image-detectable …

Simple Summary In this study, we propose a novel deep-learning method based on multi-
stage transfer learning (MSTL) from ImageNet and cancer cell line image pre-trained models …

Simple Summary Diagnosing breast cancer masses and calcification clusters is crucial in
mammography, which reduces disease consequences and initiates treatment at an early …