The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Deep learning of feature representation with multiple instance learning for medical image analysis
Abstract
This paper studies the effectiveness of accomplishing high-level tasks with a minimum of manual annotation and good feature representations for medical images. In medical image analysis, objects like cells are characterized by significant clinical features. Previously developed features like SIFT and HARR are unable to comprehensively represent such objects. Therefore, feature representation is especially important. In this paper, we study automatic extraction of feature representation through deep learning (DNN). Furthermore, detailed annotation of objects is often an ambiguous and challenging task. We use multiple instance learning (MIL) framework in classification training with deep learning features. Several interesting conclusions can be drawn from our work: (1) automatic feature learning outperforms manual feature; (2) the unsupervised approach can achieve performance that's close to fully supervised approach (93.56%) vs. (94.52%); and (3) the MIL performance of coarse label (96.30%) outweighs the supervised performance of fine label (95.40%) in supervised deep learning features.
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What is the best multi-stage architecture for object recognition?
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Deep neural networks for acoustic emotion recognition: Raising the benchmarks
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Metadata
- Authors
- Xu, Y., Mo, T., Feng, Q., Zhong, P., Lai, M., & Chang, E. I.-C.
- Venue
- 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2014-05
- Pages
- 1626-1630
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2050418754
- Cited by
- 354
- References
- 7
- FWCI
- 13.83
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Xu, Y., Mo, T., Feng, Q., Zhong, P., Lai, M., & Chang, E. I.-C. (2014). Deep learning of feature representation with multiple instance learning for medical image analysis. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp.2014.6853873
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