The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …

Abstract Vision Transformers (ViT) s have shown great performance in self-supervised
learning of global and local representations that can be transferred to downstream …

An increasing number of public datasets have shown a marked impact on automated organ
segmentation and tumor detection. However, due to the small size and partially labeled …

Abstract Fully Convolutional Neural Networks (FCNNs) with contracting and expanding
paths have shown prominence for the majority of medical image segmentation applications …

Whole abdominal organ segmentation is important in diagnosing abdomen lesions,
radiotherapy, and follow-up. However, oncologists' delineating all abdominal organs from …

Transformer-based models, capable of learning better global dependencies, have recently
demonstrated exceptional representation learning capabilities in computer vision and …

Abdominal organs play a significant role in regulating various functional systems. Any
impairment in its functioning can lead to cancerous diseases. Diagnosing these diseases …

We propose a novel teacher-student model for semi-supervised multi-organ segmentation.
In the teacher-student model, data augmentation is usually adopted on unlabeled data to …

Deep learning models have demonstrated remarkable success in multi-organ segmentation
but typically require large-scale datasets with all organs of interest annotated. However …

Accurate 3D medical imaging can effectively assist doctors in diagnosing diseases.
Currently, deep learning-based segmentation methods have yielded good results but face …