4 papers
Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification
Xinrui Zhou, Yuhao Huang, Haoran Dou +13
In the medical field, the limited availability of large-scale datasets and labor-intensive annotation processes hinder the performance of deep models. Diffusion-based generative au…
Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound
Yuhao Huang, Ao Chang, Haoran Dou +8
Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, d…
Subtyping Breast Lesions via Generative Augmentation based Long-tailed Recognition in Ultrasound
Shijing Chen, Xinrui Zhou, Yuhao Wang +4
Accurate identification of breast lesion subtypes can facilitate personalized treatment and interventions. Ultrasound (US), as a safe and accessible imaging modality, is extensivel…
EM-Net: Efficient Channel and Frequency Learning with Mamba for 3D Medical Image Segmentation
Ao Chang, Jiajun Zeng, Ruobing Huang +1
Convolutional neural networks have primarily led 3D medical image segmentation but may be limited by small receptive fields. Transformer models excel in capturing global relationsh…