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Found 5 papers, 2 papers with code
Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI $-$Should Different Clinical Objectives Mandate Different Loss Functions?
We hypothesize that probabilistic voxel-level classification of anatomy and malignancy in prostate MRI, although typically posed as near-identical segmentation tasks via U-Nets, require different loss functions for optimal performance due to inherent differences in their clinical objectives.
Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks
Furthermore, we show that the meta-loss function of M-heads improves OOD detection in terms of AUROC from 87. 4 to 88. 7.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Improved and Robust Controversy Detection in General Web Pages Using Semantic Approaches under Large Scale Conditions
This paper investigates neural classifiers as a more robust methodology for controversy detection in general web pages.
Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks
The group equivariant CNN framework is extended for segmentation by introducing a new equivariant (G->Z2)-convolution that transforms feature maps on a group to planar feature maps.
Rotation Equivariant CNNs for Digital Pathology
We propose a new model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection.
Ranked #7 on
Breast Tumour Classification
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