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Found 11 papers, 9 papers with code
Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation
The results from a multi-centric MRI dataset of 172 patients demonstrate that our proposed measures more effectively capture model errors at the lesion and patient scales compared to measures that average voxel-scale uncertainty values.
Uncovering Unique Concept Vectors through Latent Space Decomposition
By decomposing the latent space of a layer in singular vectors and refining them by unsupervised clustering, we uncover concept vectors aligned with directions of high variance that are relevant to the model prediction, and that point to semantically distinct concepts.
Disentangling Neuron Representations with Concept Vectors
Mechanistic interpretability aims to understand how models store representations by breaking down neural networks into interpretable units.
Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
We tested our method for multiple clinically and biologically relevant biomarkers: homologous repair deficiency (HRD) score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment.
Tackling Bias in the Dice Similarity Coefficient: Introducing nDSC for White Matter Lesion Segmentation
This work describes a detailed analysis of the recently proposed normalised Dice Similarity Coefficient (nDSC) for binary segmentation tasks as an adaptation of DSC which scales the precision at a fixed recall rate to tackle this bias.
Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis
This paper focuses on the uncertainty estimation for white matter lesions (WML) segmentation in magnetic resonance imaging (MRI).
Attention-based Interpretable Regression of Gene Expression in Histology
Interpretability of deep learning is widely used to evaluate the reliability of medical imaging models and reduce the risks of inaccurate patient recommendations.
Shifts 2.0: Extending The Dataset of Real Distributional Shifts
This creates a need to be able to assess how robustly ML models generalize as well as the quality of their uncertainty estimates.
Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization
Here we show that our architecture, by learning end-to-end an uncertainty-based weighting combination of multi-task and adversarial losses, is encouraged to focus on pathology features such as density and pleomorphism of nuclei, e. g. variations in size and appearance, while discarding misleading features such as staining differences.
Regression Concept Vectors for Bidirectional Explanations in Histopathology
Explanations for deep neural network predictions in terms of domain-related concepts can be valuable in medical applications, where justifications are important for confidence in the decision-making.
Studying the control of non invasive prosthetic hands over large time spans
The electromyography (EMG) signal is the electrical manifestation of a neuromuscular activation that provides access to physiological processes which cause the muscle to generate force and produce movement.
