12 citations · 26 across the 6 of their papers we have counts for
5 papers · 1 filter
Leveraging Causal Reasoning Method for Explaining Medical Image Segmentation Models
Limai Jiang, Ruitao Xie, Bokai Yang +6
Medical image segmentation plays a vital role in clinical decision-making, enabling precise localization of lesions and guiding interventions. Despite significant advances in segme…
A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor Segmentation
Ruitao Xie, Limai Jiang, Xiaoxi He +2
Machine-based brain tumor segmentation can help doctors make better diagnoses. However, the complex structure of brain tumors and expensive pixel-level annotations present challeng…
Accurate Explanation Model for Image Classifiers using Class Association Embedding
Ruitao Xie, Jingbang Chen, Limai Jiang +3
Image classification is a primary task in data analysis where explainable models are crucially demanded in various applications. Although amounts of methods have been proposed to o…
Autism Spectrum Disorder Classification with Interpretability in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder
Ruimin Ma, Ruitao Xie, Yanlin Wang +4
Autism spectrum disorder (ASD) is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients, making early screening and in…
Active Globally Explainable Learning for Medical Images via Class Association Embedding and Cyclic Adversarial Generation
Ruitao Xie, Jingbang Chen, Limai Jiang +3
Explainability poses a major challenge to artificial intelligence (AI) techniques. Current studies on explainable AI (XAI) lack the efficiency of extracting global knowledge about…