The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Classifying and segmenting microscopy images with deep multiple instance learning
Open accessAbstract
Motivation : High-content screening (HCS) technologies have enabled large scale imaging experiments for studying cell biology and for drug screening. These systems produce hundreds of thousands of microscopy images per day and their utility depends on automated image analysis. Recently, deep learning approaches that learn feature representations directly from pixel intensity values have dominated object recognition challenges. These tasks typically have a single centered object per image and existing models are not directly applicable to microscopy datasets. Here we develop an approach that combines deep convolutional neural networks (CNNs) with multiple instance learning (MIL) in order to classify and segment microscopy images using only whole image level annotations. Results : We introduce a new neural network architecture that uses MIL to simultaneously classify and segment microscopy images with populations of cells. We base our approach on the similarity between the aggregation function used in MIL and pooling layers used in CNNs. To facilitate aggregating across large numbers of instances in CNN feature maps we present the Noisy-AND pooling function, a new MIL operator that is robust to outliers. Combining CNNs with MIL enables training CNNs using whole microscopy images with image level labels. We show that training end-to-end MIL CNNs outperforms several previous methods on both mammalian and yeast datasets without requiring any segmentation steps. Availability and implementation : Torch7 implementation available upon request. Contact : oren.kraus@mail.utoronto.ca
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Cites
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MethodInfluentialSemantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
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Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Chen, R. J., Barbieri, M., & Mahmood, F. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00682-w · Cited by 2,282
The rise of deep learning in drug discovery
Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770
Deep learning for computational biology
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Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
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“Further applications of DNN explanation techniques for medical images include the detection of lesions in diabetic retinopathy data [145], pixelwise analysis of microscopy images from global image annotations [98], the validation of predictions in dermatology [199], and analysis of X-ray images [48].”
Background
Metadata
- Authors
- Kraus, O. Z., Ba, J. L., & Frey, B. J.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-06-11
- Volume
- 32
- Issue
- 12
- Pages
- i52-i59
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2269649163
- Cited by
- 411
- References
- 10
- FWCI
- 28.30
- Type
- journal article
- Language
- en
- Open access
- hybrid
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