Training segmentation models for medical images continues to be challenging due to the
limited availability of data annotations. Segment Anything Model (SAM) is a foundation …

Ovarian cancer poses a major worldwide health issue, marked by high death rates and a
deficiency in reliable diagnostic methods. The precise and prompt detection of ovarian …

Segment Anything Model (SAM) has gained significant attention because of its ability to
segment a variety of objects in images given a prompt. The recently developed SAM 2 has …

Generalization capabilities of learning-based medical image segmentation across domains
are currently limited by the performance degradation caused by the domain shift, particularly …

Semantic medical image segmentation is a crucial part of both scientific research and
clinical care. With enough labelled data, deep learning models can be trained to accurately …

Existing learning-based solutions to medical image segmentation have two important
shortcomings. First for most new segmentation tasks a new model has to be trained or fine …

Difficulty in detecting tumours in early stages is the major cause of mortalities in patients,
despite the advancements in treatment and research regarding ovarian cancer. Deep …

Diagnostic results can be radically influenced by the quality of 2D ovarian-tumor ultrasound
images. However, clinically processed 2D ovarian-tumor ultrasound images contain many …

Current state-of-the-art medical image segmentation methods prioritize accuracy but often at
the expense of increased computational demands and larger model sizes. Applying these …

Early detection is crucial for increasing the chance of survival in Ovarian Cancer (OC), as it
is a very challenging illness to treat that often leads to death. Ultrasound (UT), Magnetic …