Search Results for author:
Found 33 papers, 20 papers with code
CNN-based explanation ensembling for dataset, representation and explanations evaluation
Explainable Artificial Intelligence has gained significant attention due to the widespread use of complex deep learning models in high-stake domains such as medicine, finance, and autonomous cars.
A comparative analysis of deep learning models for lung segmentation on X-ray images
Robust and highly accurate lung segmentation in X-rays is crucial in medical imaging.
Interpretable Machine Learning for Survival Analysis
With the spread and rapid advancement of black box machine learning models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has become increasingly important over the last decade.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+4
Underestimation of lung regions on chest X-ray segmentation masks assessed by comparison with total lung volume evaluated on computed tomography
We show, that lung X-ray masks created by following the contours of the heart, mediastinum, and diaphragm significantly underestimate lung regions and exclude substantial portions of the lungs from further assessment, which may result in numerous clinical errors.
NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble Techniques
This paper presents a comprehensive comparative analysis of explainable artificial intelligence (XAI) ensembling methods.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Deep spatial context: when attention-based models meet spatial regression
The DSCon allows for a quantitative measure of the spatial context's role using three Spatial Context Measures: $SCM_{features}$, $SCM_{targets}$, $SCM_{residuals}$ to distinguish whether the spatial context is observable within the features of neighboring regions, their target values (attention scores) or residuals, respectively.
Big Tech influence over AI research revisited: memetic analysis of attribution of ideas to affiliation
This suggests that the notion of Big Tech domination over AI research is oversimplified in the discourse.
survex: an R package for explaining machine learning survival models
Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models.
Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data
If differently behaving models are detected in the Rashomon set, their combined analysis leads to more trustworthy conclusions, which is of vital importance for high-stakes applications such as medical applications.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Multi-task learning for classification, segmentation, reconstruction, and detection on chest CT scans
Lung cancer and covid-19 have one of the highest morbidity and mortality rates in the world.
The Effect of Balancing Methods on Model Behavior in Imbalanced Classification Problems
To capture these changes, Explainable Artificial Intelligence tools are used to compare models trained on datasets before and after balancing.
Explainable artificial intelligence
imbalanced classification
SeFNet: Bridging Tabular Datasets with Semantic Feature Nets
In this paper, we present an example of SeFNet prepared for a collection of predictive tasks in healthcare, with the features' relations derived from the SNOMED-CT ontology.
Prevention is better than cure: a case study of the abnormalities detection in the chest
Based on this use case, we show how to monitor data and model balance (fairness) throughout the life cycle of a predictive model, from data acquisition to parity analysis of model scores.
Towards Evaluating Explanations of Vision Transformers for Medical Imaging
Our findings provide insights into the applicability of ViT explanations in medical imaging and highlight the importance of using appropriate evaluation criteria for comparing them.
HADES: Homologous Automated Document Exploration and Summarization
This paper introduces HADES, a novel tool for automatic comparative documents with similar structures.
Climate Policy Tracker: Pipeline for automated analysis of public climate policies
The number of standardized policy documents regarding climate policy and their publication frequency is significantly increasing.
SurvSHAP(t): Time-dependent explanations of machine learning survival models
Experiments on synthetic and medical data confirm that SurvSHAP(t) can detect variables with a time-dependent effect, and its aggregation is a better determinant of the importance of variables for a prediction than SurvLIME.
Explainable expected goal models for performance analysis in football analytics
To measure the probability of a shot being a goal by the expected goal, several features are used to train an expected goal model which is based on the event and tracking football data.
Consolidated learning -- a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IV
For many machine learning models, a choice of hyperparameters is a crucial step towards achieving high performance.
LIMEcraft: Handcrafted superpixel selection and inspection for Visual eXplanations
LIMEcraft enhances the process of explanation by allowing a user to interactively select semantically consistent areas and thoroughly examine the prediction for the image instance in case of many image features.
MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence
The growing number of AI applications, also for high-stake decisions, increases the interest in Explainable and Interpretable Machine Learning (XI-ML).
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Do not explain without context: addressing the blind spot of model explanations
The increasing number of regulations and expectations of predictive machine learning models, such as so called right to explanation, has led to a large number of methods promising greater interpretability.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
Kleister: Key Information Extraction Datasets Involving Long Documents with Complex Layouts
The relevance of the Key Information Extraction (KIE) task is increasingly important in natural language processing problems.
Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models
What is even more important and valuable we also show how to boost advanced models using techniques which allow to interpret them and made them more accessible for credit risk practitioners, resolving the crucial obstacle in widespread deployment of more complex, 'black box' models like random forests, gradient boosted or extreme gradient boosted trees.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
fairmodels: A Flexible Tool For Bias Detection, Visualization, And Mitigation
The package includes a series of methods for bias mitigation that aim to diminish the discrimination in the model.
Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies
The sudden outbreak and uncontrolled spread of COVID-19 disease is one of the most important global problems today.
MementoML: Performance of selected machine learning algorithm configurations on OpenML100 datasets
Data collected in this way is used to study the factors influencing the algorithm's performance.
Does imputation matter? Benchmark for predictive models
Incomplete data are common in practical applications.
Interpretable Meta-Measure for Model Performance
For example, the difference in performance for two models has no probabilistic interpretation, there is no reference point to indicate whether they represent a significant improvement, and it makes no sense to compare such differences between data sets.
Kleister: A novel task for Information Extraction involving Long Documents with Complex Layout
State-of-the-art solutions for Natural Language Processing (NLP) are able to capture a broad range of contexts, like the sentence-level context or document-level context for short documents.
Towards explainable meta-learning
are used to predict the expected performance.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations
To our surprise, their development is driven by model developers rather than a study of needs for human end users.
Interpretable Segmentation of Medical Free-Text Records Based on Word Embeddings
Is it true that patients with similar conditions get similar diagnoses?
