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Found 13 papers, 7 papers with code
xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer
Understanding how deep learning models predict oncology patient risk can provide critical insights into disease progression, support clinical decision-making, and pave the way for trustworthy and data-driven precision medicine.
AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics
Without any specific training for the diseases, our best-performing model reliably detected a broad spectrum of infrequent ("anomalous") pathologies with 95. 0% (stomach) and 91. 0% (colon) AUROC and generalized across scanners and hospitals.
RudolfV: A Foundation Model by Pathologists for Pathologists
Artificial intelligence has started to transform histopathology impacting clinical diagnostics and biomedical research.
Leveraging weak complementary labels to improve semantic segmentation of hepatocellular carcinoma and cholangiocarcinoma in H&E-stained slides
Motivated by the medical application, we demonstrate for general segmentation tasks that including additional patches with solely weak complementary labels during model training can significantly improve the predictive performance and robustness of a model.
Balancing the composition of word embeddings across heterogenous data sets
Word embeddings capture semantic relationships based on contextual information and are the basis for a wide variety of natural language processing applications.
Explanations can be manipulated and geometry is to blame
Explanation methods aim to make neural networks more trustworthy and interpretable.
Software and application patterns for explanation methods
Building on this we show how explanation methods can be used in applications to understand predictions for miss-classified samples, to compare algorithms or networks, and to examine the focus of networks.
iNNvestigate neural networks!
The presented library iNNvestigate addresses this by providing a common interface and out-of-the- box implementation for many analysis methods, including the reference implementation for PatternNet and PatternAttribution as well as for LRP-methods.
An Empirical Study on The Properties of Random Bases for Kernel Methods
Kernel machines as well as neural networks possess universal function approximation properties.
The (Un)reliability of saliency methods
Saliency methods aim to explain the predictions of deep neural networks.
Learning how to explain neural networks: PatternNet and PatternAttribution
We show that these methods do not produce the theoretically correct explanation for a linear model.
Distributed Optimization of Multi-Class SVMs
Training of one-vs.-rest SVMs can be parallelized over the number of classes in a straight forward way.
