2015 DeepModelBasedTransferandMultiT

• (Zhang et al., 2015) ⇒ Wenlu Zhang, Rongjian Li, Tao Zeng, Qian Sun, Sudhir Kumar, Jieping Ye, and Shuiwang Ji. (2015). “Deep Model Based Transfer and Multi-Task Learning for Biological Image Analysis.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783304

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Deep+Model+Based+Transfer+and+Multi-Task+Learning+for+Biological+Image+Analysis
• http://dl.acm.org/citation.cfm?id=2783258.2783304&preflayout=flat#citedby

Quotes

Author Keywords

• Bioinformatics; data mining; deep learning; image analysis; multi-task learning; transfer learning

Abstract

A central theme in learning from image data is to develop appropriate image representations for the specific task at hand. Traditional methods used handcrafted local features combined with high-level image representations to generate image-level representations. Thus, a practical challenge is to determine what features are appropriate for specific tasks. For example, in the study of gene expression patterns in Drosophila melanogaster, texture features based on wavelets were particularly effective for determining the developmental stages from in situ hybridization(ISH) images. Such image representation is however not suitable for controlled vocabulary (CV) term annotation because each CV term is often associated with only a part of an image. Here, we developed problem-independent feature extraction methods to generate hierarchical representations for ISH images. Our approach is based on the deep convolutional neural networks (CNNs) that can act on image pixels directly. To make the extracted features generic, the models were trained using a natural image set with millions of labeled examples. These models were transferred to the ISH image domain and used directly as feature extractors to compute image representations. Furthermore, we employed multi-task learning method to fine-tune the pre-trained models with labeled ISH images, and also extracted features from the fine-tuned models. Experimental results showed that feature representations computed by deep models based on transfer and multi-task learning significantly outperformed other methods for annotating gene expression patterns at different stage ranges. We also demonstrated that the intermediate layers of deep models produced the best gene expression pattern representations.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 DeepModelBasedTransferandMultiT	Shuiwang Ji Sudhir Kumar Jieping Ye Tao Zeng Wenlu Zhang Qian Sun Rongjian Li			Deep Model Based Transfer and Multi-Task Learning for Biological Image Analysis				10.1145/2783258.2783304		2015