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Found 5 papers, 1 papers with code
Fusing Medical Image Features and Clinical Features with Deep Learning for Computer-Aided Diagnosis
Current Computer-Aided Diagnosis (CAD) methods mainly depend on medical images.
Self-Supervised Transfer Learning for Hand Mesh Recovery From Binocular Images
Traditional methods for RGB hand mesh recovery usually need to train a separate model for each dataset with the corresponding ground truth and are hardly adapted to new scenarios without the ground truth for supervision.
A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms
Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database of screening mammography (DDSM) is used for evaluation.
A New Three-stage Curriculum Learning Approach to Deep Network Based Liver Tumor Segmentation
The learning in the first stage is performed on the whole input to obtain an initial deep network for tumor segmenta-tion.
HBE: Hand Branch Ensemble Network for Real-time 3D Hand Pose Estimation
The structural design inspiration of the HBE network comes from the understanding of the differences in the functional importance of different fingers.
