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Found 7 papers, 2 papers with code
A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor Segmentation
Machine-based brain tumor segmentation can help doctors make better diagnoses.
Accurate Explanation Model for Image Classifiers using Class Association Embedding
Recombining the individual code of a given sample with altered class-associated code leads to a synthetic real-looking sample with preserved individual characters but modified class-associated features and possibly flipped class assignments.
Finding Beautiful and Happy Images for Mental Health and Well-being Applications
This paper explores how artificial intelligence (AI) technology can contribute to achieve progress on good health and well-being, one of the United Nations' 17 Sustainable Development Goals.
Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service
Then, we formulate the initial client pool selection problem into an optimization problem that aims to maximize the overall scores of selected clients within a given budget and propose a greedy algorithm to solve it.
Autism Spectrum Disorder Classification in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder
Few studies conduct machine classification of ASD for participants below 5-year-old, but, with mediocre predictive accuracy.
Active Globally Explainable Learning for Medical Images via Class Association Embedding and Cyclic Adversarial Generation
Class association embedding distills the global class-related features of all instances into a unified domain with well separation between classes.
AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography
To address this, we organized the Angle closure Glaucoma Evaluation challenge (AGE), held in conjunction with MICCAI 2019.
