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Found 5 papers, 3 papers with code
Learning A Multi-Task Transformer Via Unified And Customized Instruction Tuning For Chest Radiograph Interpretation
We first compose a multi-task training dataset comprising 13. 4 million instruction and ground-truth pairs (with approximately one million radiographs) for the customized tuning, involving both image- and pixel-level tasks.
MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset
The proposed model was pretrained with 110k unannotated 3D CT volumes, and experiments with different downstream segmentation targets including head and neck organs, thoracic/abdominal organs showed that our pretrained model largely outperformed training from scratch and several state-of-the-art self-supervised training methods and segmentation models.
One-shot Weakly-Supervised Segmentation in Medical Images
Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation.
Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images
Pulmonary vessel segmentation is important for clinical diagnosis of pulmonary diseases, while is also challenging due to the complicated structure.
SenseCare: A Research Platform for Medical Image Informatics and Interactive 3D Visualization
To this end, we have developed SenseCare research platform, which is designed to facilitate translational research on intelligent diagnosis and treatment planning in various clinical scenarios.
Human-Computer Interaction Image and Video Processing
