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Found 10 papers, 8 papers with code
Highly accurate protein structure prediction with AlphaFold
Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics.
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning.
A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients
Through training on unlabeled data, anomaly detection has the potential to impact computer-aided diagnosis by outlining suspicious regions.
Reg R-CNN: Lesion Detection and Grading under Noisy Labels
To this end, we propose Reg R-CNN, which replaces the second-stage classification model of a current object detector with a regression model.
Deep Probabilistic Modeling of Glioma Growth
Existing approaches to modeling the dynamics of brain tumor growth, specifically glioma, employ biologically inspired models of cell diffusion, using image data to estimate the associated parameters.
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
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.
Automated Design of Deep Learning Methods for Biomedical Image Segmentation
Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning.
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection
Unsupervised learning can leverage large-scale data sources without the need for annotations.
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection
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.
A Probabilistic U-Net for Segmentation of Ambiguous Images
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.
