Transfer learning from supervised ImageNet models has been frequently used in medical image analysis.
More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging.
Ranked #1 on Pulmonary Embolism Detection on PE-CAD FPRED
Qualitative and quantitative evaluations demonstrate that the proposed method outperforms the state of the art in multi-domain image-to-image translation and that it surpasses predominant weakly-supervised localization methods in both disease detection and localization.
Simulation results show that the proposed sparse approximation method has the real-time solutions with satisfactory MSEs.
The aim of this paper is to train an RBF neural network and select centers under concurrent faults.