A patch distribution-based active learning method for multiple instance Alzheimer's disease diagnosis

Tianxiang Wang, Qun Dai

Abstract: Highlights•We introduce a block-wise Hash difference measure to replace Euclidean distance, which significantly preserves spatial and structural information within each patch.•We design a Patch-Level Global and Local Attention-based Multi-Instance deep Learning Model that utilizes attention mechanisms to enhance Alzheimer's disease diagnosis performance and interpretability.•We construct a Patch-Level Instance Distribution-based Active Learning Strategy which aim at selecting the least discriminative samples from the candidate data set based on both sample-level and decision-level Gaussian distributions, and incorporating them into the training set to minimize labeling costs.•Experiments demonstrated the effectiveness of our proposed methods.