1 research outputs found
Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities
Elastography ultrasound (EUS) provides additional bio-mechanical in-formation
about lesion for B-mode ultrasound (BUS) in the diagnosis of breast cancers.
However, joint utilization of both BUS and EUS is not popular due to the lack
of EUS devices in rural hospitals, which arouses a novel modality im-balance
problem in computer-aided diagnosis (CAD) for breast cancers. Current transfer
learning (TL) pay little attention to this special issue of clinical modality
imbalance, that is, the source domain (EUS modality) has fewer labeled samples
than those in the target domain (BUS modality). Moreover, these TL methods
cannot fully use the label information to explore the intrinsic relation
between two modalities and then guide the promoted knowledge transfer. To this
end, we propose a novel doubly supervised TL network (DDSTN) that integrates
the Learning Using Privileged Information (LUPI) paradigm and the Maximum Mean
Discrepancy (MMD) criterion into a unified deep TL framework. The proposed
algorithm can not only make full use of the shared labels to effectively guide
knowledge transfer by LUPI paradigm, but also perform additional super-vised
transfer between unpaired data. We further introduce the MMD criterion to
enhance the knowledge transfer. The experimental results on the breast
ultra-sound dataset indicate that the proposed DDSTN outperforms all the
compared state-of-the-art algorithms for the BUS-based CAD.Comment: Accepted by MICCAI 202