While domain-specific data augmentation can be useful in training neural networks for
medical imaging tasks, such techniques have not been widely used to date. Our objective …

The rapid evolution of deep learning has significantly advanced the field of medical image
analysis. However, despite these achievements, the further enhancement of deep learning …

Modern deep learning methods for semantic segmentation require labor-intensive labeling
for large-scale datasets with dense pixel-level annotations. Recent data augmentation …

Automated semantic segmentation in colonoscopy is crucial for detecting colon polyps and
preventing the development of colorectal cancer. However, the scarcity of annotated data …

Instance-level perception tasks like object detection, instance segmentation, and 3D
detection require many training samples to achieve satisfactory performance. The …

The medical datasets are usually faced with the problem of scarcity and data imbalance.
Moreover, annotating large datasets for semantic segmentation of medical lesions is domain …

Obtaining labelled data in medical image segmentation is challenging due to the need for
pixel-level annotations by experts. Recent works have shown that augmenting the object of …

Precise liver tumors and associated organ segmentation hold immense value for surgical
and radiological intervention, enabling anatomical localization for pre-operative planning …

Semi-supervised learning has the potential to improve the data-efficiency of training data-
hungry deep neural networks, which is especially important for medical image analysis tasks …

Accurate brain lesion delineation is important for planning neurosurgical treatment.
Automatic brain lesion segmentation methods based on convolutional neural networks have …