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cs.LG2024
Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representations
Sameer Ambekar, Julia A. Schnabel, Cosmin I. Bercea
Deep learning models in medical imaging often encounter challenges when adapting to new clinical settings unseen during training. Test-time adaptation offers a promising approach t…
eess.IV2024★ 1 cited
Robustness Testing of Black-Box Models Against CT Degradation Through Test-Time Augmentation
Jack Highton, Quok Zong Chong, Samuel Finestone +3
Deep learning models for medical image segmentation and object detection are becoming increasingly available as clinical products. However, as details are rarely provided about the…