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Found 5 papers, 2 papers with code
Structured Model Pruning for Efficient Inference in Computational Pathology
In this work, we demonstrate that model pruning, as a model compression technique, can effectively reduce inference cost for computational and digital pathology based analysis with a negligible loss of analysis performance.
Cross-modality Attention-based Multimodal Fusion for Non-small Cell Lung Cancer (NSCLC) Patient Survival Prediction
Compared with single modality, which achieved c-index of 0. 5772 and 0. 5885 using solely tissue image data or RNA-seq data, respectively, the proposed fusion approach achieved c-index 0. 6587 in our experiment, showcasing the capability of assimilating modality-specific knowledge from varied modalities.
Why Should we Combine Training and Post-Training Methods for Out-of-Distribution Detection?
Deep neural networks are known to achieve superior results in classification tasks.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Simultaneous Classification and Out-of-Distribution Detection Using Deep Neural Networks
Deep neural networks have achieved great success in classification tasks during the last years.
Outlier Exposure with Confidence Control for Out-of-Distribution Detection
Deep neural networks have achieved great success in classification tasks during the last years.
Ranked #1 on
Out-of-Distribution Detection
on CIFAR-100 vs SVHN
(using extra training data)
