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Found 8 papers, 4 papers with code
Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction
However, the optimal approach for explicit alignment in mammography remains underexplored.
Natural Language Processing for Electronic Health Records in Scandinavian Languages: Norwegian, Swedish, and Danish
Objective: The study aims to perform a systematic review to comprehensively assess and analyze the state-of-the-art NLP methods for the mainland Scandinavian clinical text.
Better, Not Just More: Data-Centric Machine Learning for Earth Observation
These examples provide concrete steps to act on geospatial data with data-centric machine learning approaches.
Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation
Precise ischemic lesion segmentation plays an essential role in improving diagnosis and treatment planning for ischemic stroke, one of the prevalent diseases with the highest mortality rate.
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
The need for interpretable models has fostered the development of self-explainable classifiers.
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels
Motivated by this, and the observation that the foreground class (e. g., one organ) is relatively homogeneous, we propose a novel anomaly detection-inspired approach to few-shot medical image segmentation in which we refrain from modeling the background explicitly.
Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation
The recent trend of integrating multi-source Chest X-Ray datasets to improve automated diagnostics raises concerns that models learn to exploit source-specific correlations to improve performance by recognizing the source domain of an image rather than the medical pathology.
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation
Current machine learning models have shown high efficiency in solving a wide variety of real-world problems.
