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Found 6 papers, 1 papers with code
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.
A deep learning algorithm for reducing false positives in screening mammography
This work lays the foundation for semi-autonomous breast cancer screening systems that could benefit patients and healthcare systems by reducing false positives, unnecessary procedures, patient anxiety, and expenses.
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.
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 Multi-site Study of a Breast Density Deep Learning Model for Full-field Digital Mammography Images and Synthetic Mammography Images
Conclusion: A BI-RADS breast density DL model demonstrated strong performance on FFDM and SM images from two institutions without training on SM images and improved using few SM images.
