Trainable Summarization to Improve Breast Tomosynthesis Classification

Digital Breast Tomosynthesis (DBT) is an emerging imaging technique for breast cancer screening aiming to overcome certain limitations of traditional mammography, such as the superimposition of tissues. On the downside, DBT increases the radiologists’ workload as it generates stacks of high-resolution images, which are time-consuming to review and annotate. In this work, we propose a deep- multiple-instance-based method for DBT volume classification that relies on the local summarization of DBT slices (referred to as slabbing) and only requires volume-wise labels for training. Slabbing offers several advantages: i) it reduces the classifier’s computational complexity across the depth, letting it focus on the higher transversal resolution. Thanks to this strategy, we are the first to train a method at almost full-resolution (as high as 120 \(\times \) 2500 \(\times \) 2000); ii) it produces slabs that are closer to standard mammography, favoring an efficient transfer from classifiers trained on larger mammography databases; and iii) the slabs combined with a Multiple-Instance Learning (MIL) classifier result in localized information favoring interpretability. The proposed slabbing MIL approach is also novel for the automatic classification of DBTs. Moreover, we propose a trainable alternative to the handcrafted slabbing algorithms based on slice-wise attention that improves performance. We perform an experimental validation on a subset of the public BCS-DBT dataset and achieve an AUC of 0.73 with five-fold cross-validation. On a private multi-vendor dataset we obtain a similar AUC of 0.73, demonstrating an excellent performance consistency.