  described by multiple instances (e.g., image patches) and simultaneously
associated with multiple labels. 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. To resolve these issues, a new single-stage framework called
broad multi-instance multi-label learning (BMIML) is proposed. In BMIML, there
are three innovative modules: i) an auto-weighted label enhancement learning
(AWLEL) based on broad learning system (BLS) is designed, which simultaneously
and efficiently captures the inter-label correlations while traditional BLS
cannot; ii) A specific MIML neural network called scalable multi-instance
probabilistic regression (SMIPR) is constructed to effectively estimate the
inter-instance correlations using the object label only, which can provide
additional probabilistic information for learning; iii) Finally, an interactive
decision optimization (IDO) is designed to combine and optimize the results
from AWLEL and SMIPR and form a single-stage framework. Experiments show that
BMIML is highly competitive to (or even better than) existing methods in
accuracy and much faster than most MIML methods even for large medical image
data sets (> 90K images).
