Abstract
Discriminative models that require full supervision are inefficacious in the medical imaging domain when large labeled datasets are unavailable. By contrast, generative modeling—i.e., learning data generation and classification—facilitates semi-supervised training with limited labeled data. Moreover, generative modeling can be advantageous in accomplishing multiple objectives for better generalization. We propose a novel multi-task learning model for jointly learning a classifier and a segmentor, from chest X-ray images, through semi-supervised learning. In addition, we propose a new loss function that combines absolute KL divergence with Tversky loss (KLTV) to yield faster convergence and better segmentation performance. Based on our experimental results using a novel segmentation model, an Adversarial Pyramid Progressive Attention U-Net (APPAU-Net), we hypothesize that KLTV can be more effective for generalizing multi-tasking models while being competitive in segmentation-only tasks.
Original language | English |
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Title of host publication | Machine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Proceedings |
Editors | Heung-Il Suk, Mingxia Liu, Chunfeng Lian, Pingkun Yan |
Pages | 151-159 |
Number of pages | 9 |
DOIs | |
State | Published - 2019 |
Event | 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: Oct 13 2019 → Oct 13 2019 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11861 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 |
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Country/Territory | China |
City | Shenzhen |
Period | 10/13/19 → 10/13/19 |
Bibliographical note
Publisher Copyright:© 2019, Springer Nature Switzerland AG.
Keywords
- Chest X-ray
- Classification
- Generative modeling
- KL-Tversky loss
- Multi-tasking
- Segmentation
- Semi-supervised
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science