5 papers · 1 filter
Evaluating Few-Shot Pill Recognition Under Visual Domain Shift
W. I. Chu, G. Tarroni, L. Li
Adverse drug events are a significant source of preventable harm, which has led to the development of automated pill recognition systems to enhance medication safety. Real-world de…
A dataset of medication images with instance segmentation masks for preventing adverse drug events
W. I. Chu, S. Hirani, G. Tarroni +1
Medication errors and adverse drug events (ADEs) pose significant risks to patient safety, often arising from difficulties in reliably identifying pharmaceuticals in real-world set…
Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly Detection
Sergio Naval Marimont, Vasilis Siomos, Matthew Baugh +3
Unsupervised Anomaly Detection (UAD) methods aim to identify anomalies in test samples comparing them with a normative distribution learned from a dataset known to be anomaly-free.…
DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection
Sergio Naval Marimont, Matthew Baugh, Vasilis Siomos +3
Unsupervised Anomaly Detection (UAD) techniques aim to identify and localize anomalies without relying on annotations, only leveraging a model trained on a dataset known to be free…
ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification
Vasilis Siomos, Sergio Naval-Marimont, Jonathan Passerat-Palmbach +1
Federated Learning (FL) is a collaborative training paradigm that allows for privacy-preserving learning of cross-institutional models by eliminating the exchange of sensitive data…