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.
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.
MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation
Our controlled experiments show that the keys to improving downstream performance on disease classification are (1) using patient metadata to appropriately create positive pairs from different images with the same underlying pathologies, and (2) maximizing the number of different images used in query pairing.
