Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification

no code implementations • 6 Sep 2022 • Qi Lai, Jianhang Zhou, Yanfen Gan, Chi-Man Vong, DeShuang Huang

Existing MIML methods are useful in many applications but most of which suffer from relatively low accuracy and training efficiency due to several issues: i) the inter-label correlations(i. e., the probabilistic correlations between the multiple labels corresponding to an object) are neglected; ii) the inter-instance correlations (i. e., the probabilistic correlations of different instances in predicting the object label) cannot be learned directly (or jointly) with other types of correlations due to the missing instance labels; iii) diverse inter-correlations (e. g., inter-label correlations, inter-instance correlations) can only be learned in multiple stages.

Image Classification Medical Image Classification +1