Search Results for author:
Found 5 papers, 1 papers with code
M&M: Tackling False Positives in Mammography with a Multi-view and Multi-instance Learning Sparse Detector
Deep-learning-based object detection methods show promise for improving screening mammography, but high rates of false positives can hinder their effectiveness in clinical practice.
Problems and shortcuts in deep learning for screening mammography
The original model trained on both datasets achieved a 0. 945 AUC on the combined US+UK dataset but paradoxically only 0. 838 and 0. 892 on the US and UK datasets, respectively.
Deep is a Luxury We Don't Have
In this paper, we tackle this complexity by leveraging a linear self-attention approximation.
Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis
In particular, we use average histogram matching (HM) and DL fine-tuning methods to generalize a FFDM model to the 2D maximum intensity projection (MIP) of DBT images.
A Hypersensitive Breast Cancer Detector
In this work, we leverage a large set of FFDM images with loose bounding boxes of mammographically significant findings to train a deep learning detector with extreme sensitivity.
