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described by multiple instances (e.g., image patches) and simultaneouslyassociated with multiple labels. Existing MIML methods are useful in manyapplications but most of which suffer from relatively low accuracy and trainingefficiency due to several issues: i) the inter-label correlations(i.e., theprobabilistic correlations between the multiple labels corresponding to anobject) are neglected; ii) the inter-instance correlations (i.e., theprobabilistic correlations of different instances in predicting the objectlabel) cannot be learned directly (or jointly) with other types of correlationsdue to the missing instance labels; iii) diverse inter-correlations (e.g.,inter-label correlations, inter-instance correlations) can only be learned inmultiple stages. To resolve these issues, a new single-stage framework calledbroad multi-instance multi-label learning (BMIML) is proposed. In BMIML, thereare three innovative modules: i) an auto-weighted label enhancement learning(AWLEL) based on broad learning system (BLS) is designed, which simultaneouslyand efficiently captures the inter-label correlations while traditional BLScannot; ii) A specific MIML neural network called scalable multi-instanceprobabilistic regression (SMIPR) is constructed to effectively estimate theinter-instance correlations using the object label only, which can provideadditional probabilistic information for learning; iii) Finally, an interactivedecision optimization (IDO) is designed to combine and optimize the resultsfrom AWLEL and SMIPR and form a single-stage framework. Experiments show thatBMIML is highly competitive to (or even better than) existing methods inaccuracy and much faster than most MIML methods even for large medical imagedata sets (> 90K images).

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