Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics.
Through training on unlabeled data, anomaly detection has the potential to impact computer-aided diagnosis by outlining suspicious regions.
To this end, we propose Reg R-CNN, which replaces the second-stage classification model of a current object detector with a regression model.
Medical imaging only indirectly measures the molecular identity of the tissue within each voxel, which often produces only ambiguous image evidence for target measures of interest, like semantic segmentation.
Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning.
Unsupervised learning can leverage large-scale data sources without the need for annotations.
The proposed architecture recaptures discarded supervision signals by complementing object detection with an auxiliary task in the form of semantic segmentation without introducing the additional complexity of previously proposed two-stage detectors.
To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses.