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Found 7 papers, 2 papers with code
Multiple Class Novelty Detection Under Data Distribution Shift
To this end, we consider the problem of multiple class novelty detection under dataset distribution shift to improve the novelty detection performance.
Segmentation of diagnostic tissue compartments on whole slide images with renal thrombotic microangiopathies (TMAs)
The thrombotic microangiopathies (TMAs) manifest in renal biopsy histology with a broad spectrum of acute and chronic findings.
Multiplexed Immunofluorescence Brain Image Analysis Using Self-Supervised Dual-Loss Adaptive Masked Autoencoder
Reliable large-scale cell detection and segmentation is the fundamental first step to understanding biological processes in the brain.
Multimodal Breast Lesion Classification Using Cross-Attention Deep Networks
Would clinical features significantly improve breast lesion classification compared to using mammograms alone?
Deep reinforcement learning in medical imaging: A literature review
Deep reinforcement learning (DRL) augments the reinforcement learning framework, which learns a sequence of actions that maximizes the expected reward, with the representative power of deep neural networks.
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks
In this paper, we develop a theoretical framework to approximate Bayesian inference for DNNs by imposing a Bernoulli distribution on the model weights.
Generalized Domain-Adaptive Dictionaries
Data-driven dictionaries have produced state-of-the-art results in various classification tasks.
