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Found 20 papers, 11 papers with code
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding
Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa.
Corpus Considerations for Annotator Modeling and Scaling
We introduce a composite embedding approach and show distinct differences in which model performs best as a function of the agreement with a given dataset.
Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challenges
Specifically, we apply three state-of-the-art prototype-based models, ProtoPNet, BRAIxProtoPNet++ and PIP-Net on mammography images for breast cancer prediction and evaluate these models w. r. t.
PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans
Information from neuroimaging examinations (CT, MRI) is increasingly used to support diagnoses of dementia, e. g., Alzheimer's disease.
A Second Look on BASS -- Boosting Abstractive Summarization with Unified Semantic Graphs -- A Replication Study
We present a detailed replication study of the BASS framework, an abstractive summarization system based on the notion of Unified Semantic Graphs.
The Queen of England is not England's Queen: On the Lack of Factual Coherency in PLMs
In this work, we consider a complementary aspect, namely the coherency of factual knowledge in PLMs, i. e., how often can PLMs predict the subject entity given its initial prediction of the object entity.
InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification
Text simplification aims to make technical texts more accessible to laypeople but often results in deletion of information and vagueness.
Give Me the Facts! A Survey on Factual Knowledge Probing in Pre-trained Language Models
Our contributions are: (1) We propose a categorization scheme for factual probing methods that is based on how their inputs, outputs and the probed PLMs are adapted; (2) We provide an overview of the datasets used for factual probing; (3) We synthesize insights about knowledge retention and prompt optimization in PLMs, analyze obstacles to adopting PLMs as knowledge bases and outline directions for future work.
Weakly Supervised Learning for Breast Cancer Prediction on Mammograms in Realistic Settings
We show that two-level MIL can be applied in realistic clinical settings where only case labels, and a variable number of images per patient are available.
Is Last Layer Re-Training Truly Sufficient for Robustness to Spurious Correlations?
Models trained with empirical risk minimization (ERM) are known to learn to rely on spurious features, i. e., their prediction is based on undesired auxiliary features which are strongly correlated with class labels but lack causal reasoning.
Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis
Automatically summarizing radiology reports into a concise impression can reduce the manual burden of clinicians and improve the consistency of reporting.
Interpreting and Correcting Medical Image Classification with PIP-Net
We conclude that part-prototype models are promising for medical applications due to their interpretability and potential for advanced model debugging.
PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification
Driven by the principle of explainability-by-design, we introduce PIP-Net (Patch-based Intuitive Prototypes Network): an interpretable image classification model that learns prototypical parts in a self-supervised fashion which correlate better with human vision.
Patient-friendly Clinical Notes: Towards a new Text Simplification Dataset
Automatic text simplification can help patients to better understand their own clinical notes.
Explaining Machine Learning Models in Natural Conversations: Towards a Conversational XAI Agent
In this work, we show how to incorporate XAI in a conversational agent, using a standard design for the agent comprising natural language understanding and generation components.
Explainable Artificial Intelligence (XAI)
Natural Language Understanding
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
Our so-called Co-12 properties serve as categorization scheme for systematically reviewing the evaluation practices of more than 300 papers published in the last 7 years at major AI and ML conferences that introduce an XAI method.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Towards a trustworthy, secure and reliable enclave for machine learning in a hospital setting: The Essen Medical Computing Platform (EMCP)
AI/Computing at scale is a difficult problem, especially in a health care setting.
Competitive Influence Maximization: Integrating Budget Allocation and Seed Selection
In this work, we integrate these two lines of research and propose a new scenario where competition happens in two phases.
Social and Information Networks Computer Science and Game Theory
Parallel Total Variation Distance Estimation with Neural Networks for Merging Over-Clusterings
We consider the initial situation where a dataset has been over-partitioned into $k$ clusters and seek a domain independent way to merge those initial clusters.
Policy Learning for Malaria Control
In this report, we introduce the progress to learn the policy for Malaria Control as a Reinforcement Learning problem in the KDD Cup Challenge 2019 and propose diverse solutions to deal with the limited observations problem.
