Computer vision models normally witness degraded performance when deployed in real-
world scenarios due to unexpected changes in inputs that were not accounted for during …

Breast lesion segmentation of ultrasound images plays a crucial role in early screening and
diagnosis of breast lesions. However, accurately segmenting lesions in breast ultrasound …

The rise of deep neural networks has effectively improved the performance of optic disc and
cup segmentation, relying on the assumption of shared data distributions between training …

Accurate depth perception is crucial for patient outcomes in endoscopic surgery, yet it is
compromised by image distortions common in surgical settings. To tackle this issue, our …

Deep learning (DL) models play a crucial role in medical image analysis, with their
performance reliant on the scale and diversity of available training data. However, medical …

In the realm of machine learning, ensuring the robustness of models against adversarial
attacks is critical, particularly in applications such as healthcare, autonomous systems and …

For neural networks, challenges arise when deploying models in real-world scenarios, as
unforeseen changes in inputs can lead to diminished performance. While data …