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Found 29 papers, 19 papers with code
Assessing the importance of long-range correlations for deep-learning-based sleep staging
This study aims to elucidate the significance of long-range correlations for deep-learning-based sleep staging.
Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks
The R-OMS score enables a systematic comparison of occlusion strategies and resolves the disagreement problem by grouping consistent PF rankings.
Cardiac and extracardiac discharge diagnosis prediction from emergency department ECGs using deep learning
Current deep learning algorithms designed for automatic ECG analysis have exhibited notable accuracy.
Using explainable AI to investigate electrocardiogram changes during healthy aging -- from expert features to raw signals
In this paper we present the following contributions: (1) We employ a deep-learning model and a tree-based model to analyze ECG data from a robust dataset of healthy individuals across varying ages in both raw signals and ECG feature format.
S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models
Scoring sleep stages in polysomnography recordings is a time-consuming task plagued by significant inter-rater variability.
Insights Into the Inner Workings of Transformer Models for Protein Function Prediction
Motivation: We explored how explainable artificial intelligence (XAI) can help to shed light into the inner workings of neural networks for protein function prediction, by extending the widely used XAI method of integrated gradients such that latent representations inside of transformer models, which were finetuned to Gene Ontology term and Enzyme Commission number prediction, can be inspected too.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadata
These components consistently enhance performance beyond the existing state-of-the-art, which is predominantly based on convolutional models.
Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery
Furthermore, we demonstrate how these XAI techniques can be utilized for knowledge discovery, such as identifying subtypes of myocardial infarction.
ECG Feature Importance Rankings: Cardiologists vs. Algorithms
Feature importance methods promise to provide a ranking of features according to importance for a given classification task.
Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees
For the trustworthy application of XAI, in particular for high-stake decisions, a more global model understanding is required.
Diffusion-based Conditional ECG Generation with Structured State Space Models
Synthetic data generation is a promising solution to address privacy issues with the distribution of sensitive health data.
Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models
The field of deep-learning-based ECG analysis has been largely dominated by convolutional architectures.
Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models
The imputation of missing values represents a significant obstacle for many real-world data analysis pipelines.
From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology
While machine learning is currently transforming the field of histopathology, the domain lacks a comprehensive evaluation of state-of-the-art models based on essential but complementary quality requirements beyond a mere classification accuracy.
Sparse Subspace Clustering for Concept Discovery (SSCCD)
We empirically demonstrate the soundness of the proposed Sparse Subspace Clustering for Concept Discovery (SSCCD) method for a variety of different image classification tasks.
On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy
Our results indicate that models initialized from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
Predicting the Binding of SARS-CoV-2 Peptides to the Major Histocompatibility Complex with Recurrent Neural Networks
Predicting the binding of viral peptides to the major histocompatibility complex with machine learning can potentially extend the computational immunology toolkit for vaccine development, and serve as a key component in the fight against a pandemic.
Self-supervised representation learning from 12-lead ECG data
In a first step, we learn contrastive representations and evaluate their quality based on linear evaluation performance on a recently established, comprehensive, clinical ECG classification task.
PredDiff: Explanations and Interactions from Conditional Expectations
PredDiff is a model-agnostic, local attribution method that is firmly rooted in probability theory.
Generative Neural Samplers for the Quantum Heisenberg Chain
Generative neural samplers offer a complementary approach to Monte Carlo methods for problems in statistical physics and quantum field theory.
Inferring respiratory and circulatory parameters from electrical impedance tomography with deep recurrent models
Electrical impedance tomography (EIT) is a noninvasive imaging modality that allows a continuous assessment of changes in regional bioimpedance of different organs.
Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL
Electrocardiography is a very common, non-invasive diagnostic procedure and its interpretation is increasingly supported by automatic interpretation algorithms.
Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning
Machine learning has the potential to aid our understanding of phase structures in lattice quantum field theories through the statistical analysis of Monte Carlo samples.
Asymptotically unbiased estimation of physical observables with neural samplers
We propose a general framework for the estimation of observables with generative neural samplers focusing on modern deep generative neural networks that provide an exact sampling probability.
Achieving Generalizable Robustness of Deep Neural Networks by Stability Training
We study the recently introduced stability training as a general-purpose method to increase the robustness of deep neural networks against input perturbations.
Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling
In this comment on "Solving Statistical Mechanics Using Variational Autoregressive Networks" by Wu et al., we propose a subtle yet powerful modification of their approach.
Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G
We investigate Early Hybrid Automatic Repeat reQuest (E-HARQ) feedback schemes enhanced by machine learning techniques as a path towards ultra-reliable and low-latency communication (URLLC).
Detecting and interpreting myocardial infarction using fully convolutional neural networks
Objective: We aim to provide an algorithm for the detection of myocardial infarction that operates directly on ECG data without any preprocessing and to investigate its decision criteria.
FormTracer - A Mathematica Tracing Package Using FORM
We present FormTracer, a high-performance, general purpose, easy-to-use Mathematica tracing package which uses FORM.
High Energy Physics - Phenomenology High Energy Physics - Theory
