The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings
Open accessAbstract
We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared with grid sampling and uniform sampling techniques, it achieves higher generalization performance. We validate the strategy on two artificial datasets and two histological datasets for breast cancer and sun exposure classification.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification
Hou, L., Samaras, D., Kurc, T. M., Gao, Y., Davis, J. E., & Saltz, J. H. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.266 · Cited by 866
A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project
Carithers, L. J., Ardlie, K., Barcus, M., Branton, P. A., Britton, A., Buia, S. A., Compton, C. C., DeLuca, D. S., Peter-Demchok, J., Gelfand, E. T., et al. (2015) · Biopreservation and Biobanking · doi:10.1089/bio.2015.0032 · Cited by 833
Attention-based Deep Multiple Instance Learning
Ilse, M., Tomczak, J. M., & Welling, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04712 · Cited by 636
Revisiting multiple instance neural networks
Wang, X., Yan, Y., Tang, P., Bai, X., & Liu, W. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.08.026 · Cited by 498
Fully Convolutional Multi-Class Multiple Instance Learning
Pathak, D., Shelhamer, E., Long, J., & Darrell, T. J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.7144 · Cited by 236
Multi-instance multi-label learning for whole slide breast histopathology
Mercan, C., Mercan, E., Aksoy, S., et al. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2216458 · Cited by 13
Cited by
A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
Hamishebahar, Y., Guan, H., So, S., & Jo, J. (2022) · Applied Sciences · doi:10.3390/app12031374 · Cited by 188
Alzubaidi, M., Agus, M., Shah, U., Makhlouf, M., Alyafei, K., & Househ, M. (2022) · Diagnostics · doi:10.3390/diagnostics12092229 · Cited by 35
Metadata
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4289085709
- Cited by
- 284
- References
- 8
- FWCI
- 12.44
- Type
- conference paper
- Language
- en
- Open access
- green
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- Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings. (2018). Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-00889-5
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