Pretraining CNN models (i.e., UNet) through self-supervision has become a powerful approach to facilitate medical image segmentation under low annotation regimes.

In this paper, we introduce a new type of model backdoor: the privacy backdoor attack .

We propose reconstruction advantage measures to audit label privatization mechanisms.

Missing values in real-world data pose a significant and unique challenge to algorithmic fairness.

One challenging modality for real-time processing is multi-photon calcium imaging (CI).