An end-to-end deep learning model can detect the gist of the abnormal in prior mammograms as perceived by experienced radiologists
A retrospective comparative study of reading performances between radiologists from two countries in the assessment of 3D mammography
Breast cancer risk prediction in Chinese women based on mammographic texture and a comprehensive set of epidemiologic factors
Expert radiologist performance does not appear to impact upon their capability in perceiving the gist of the abnormal on mammograms
Does the strength of the gist signal predict the difficulty of breast cancer detection in usual presentation and reporting mechanisms?
A framework for distinguishing benign from malignant breast histopathological images using deep residual networks
Determining local and contextual features describing appearance of difficult to identify mitotic figures
Predicting radiologists' true and false positive decisions in reading mammograms by using gaze parameters and image-based features