Publication
ISBI 2018
Conference paper

Universal multi-modal deep network for classification and segmentation of medical images

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Abstract

Medical image processing algorithms have traditionally focused on a specific problem or disease per modality. This approach has continued with the wide-spread adoption of deep learning in the last 5 years. Building a system with multiple neural networks and different specialized image processing algorithms is a challenge as each network requires a lot of memory and is computationally heavy. More importantly, cascading multiple networks propagates errors from one stage to another reducing overall system accuracy. In this work, we propose a single universal network that can: 1) segment different organs across different modalities, and 2) solve both segmentation and classification problems simultaneously. We compare our approach with traditional segmentation network for each modality. Our results showed modality/viewpoint classification accuracy of 99% and average dice score of 0.89 for segmentation accuracy. The proposed network can be further developed to include segmentation of more organs and disease classification.

Date

23 May 2018

Publication

ISBI 2018

Authors

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