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Found 20 papers, 10 papers with code
Exploiting XAI maps to improve MS lesion segmentation and detection in MRI
To date, several methods have been developed to explain deep learning algorithms for classification tasks.
Instance-level quantitative saliency in multiple sclerosis lesion segmentation
Saliency maps (based on SmoothGrad) in FLAIR showed positive values inside a lesion and negative in its neighborhood.
EDUE: Expert Disagreement-Guided One-Pass Uncertainty Estimation for Medical Image Segmentation
Deploying deep learning (DL) models in medical applications relies on predictive performance and other critical factors, such as conveying trustworthy predictive uncertainty.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
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.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images
The comparison of the PFS prediction performance in Tasks 2 and 3 suggests that providing the GTVt contour was not crucial to achieve best results, which indicates that fully automatic methods can be used.
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.
Standardised convolutional filtering for radiomics
Here we present a complete version of a reference manual on the use of convolutional filters in radiomics and quantitative image analysis.
3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis
We investigate the benefits of using the bispectrum over the spectrum in the design of a LRI layer embedded in a shallow Convolutional Neural Network (CNN) for 3D image analysis.
Local Rotation Invariance in 3D CNNs
Locally Rotation Invariant (LRI) image analysis was shown to be fundamental in many applications and in particular in medical imaging where local structures of tissues occur at arbitrary rotations.
Automatic Segmentation of Head and Neck Tumors and Nodal Metastases in PET-CT scans
Radiomics, the prediction of disease characteristics using quantitative image biomarkers from medical images, relies on expensive manual annotations of Regions of Interest (ROI) to focus the analysis.
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.
Rotational 3D Texture Classification Using Group Equivariant CNNs
Convolutional Neural Networks (CNNs) traditionally encode translation equivariance via the convolution operation.
Fast Rotational Sparse Coding
In this work, we propose an algorithm for a rotational version of sparse coding that is based on K-SVD with additional rotation operations.
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification
Deep learning methods have shown impressive results and are now the new state of the art for a wide range of computer vision tasks including image and video recognition and segmentation.
Texture segmentation with Fully Convolutional Networks
We show in particular that these networks can learn to recognize and segment a type of texture, e. g. wood and grass from texture recognition datasets (no training segmentation).
Using Filter Banks in Convolutional Neural Networks for Texture Classification
Its architecture is indeed well suited to object analysis by learning and classifying complex (deep) features that represent parts of an object or the object itself.
