Search Results for author:
Found 7 papers, 5 papers with code
Slightly Shift New Classes to Remember Old Classes for Video Class-Incremental Learning
So we propose SNRO, which slightly shifts the features of new classes to remember old classes.
BAAF: A Benchmark Attention Adaptive Framework for Medical Ultrasound Image Segmentation Tasks
The AI-based assisted diagnosis programs have been widely investigated on medical ultrasound images.
Towards Continual Egocentric Activity Recognition: A Multi-modal Egocentric Activity Dataset for Continual Learning
However, the deficiency of related dataset hinders the development of multi-modal deep learning for egocentric activity recognition.
ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentation
Breast cancer is one of the common cancers that endanger the health of women globally.
Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentation
However, variable tumor morphology, blurred boundary, and similar intensity distributions bring challenges for accurate segmentation of breast tumors.
BAGNet: Bidirectional Aware Guidance Network for Malignant Breast lesions Segmentation
Breast lesions segmentation is an important step of computer-aided diagnosis system, and it has attracted much attention.
AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images
Different from existing attention mechanisms, the hybrid adaptive attention module can guide the network to adaptively select more robust representation in channel and space dimensions to cope with more complex breast lesions segmentation.
