The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A Dataset for Breast Cancer Histopathological Image Classification
Abstract
Today, medical image analysis papers require solid experiments to prove the usefulness of proposed methods. However, experiments are often performed on data selected by the researchers, which may come from different institutions, scanners, and populations. Different evaluation measures may be used, making it difficult to compare the methods. In this paper, we introduce a dataset of 7909 breast cancer histopathology images acquired on 82 patients, which is now publicly available from http://web.inf.ufpr.br/vri/breast-cancer-database. The dataset includes both benign and malignant images. The task associated with this dataset is the automated classification of these images in two classes, which would be a valuable computer-aided diagnosis tool for the clinician. In order to assess the difficulty of this task, we show some preliminary results obtained with state-of-the-art image classification systems. The accuracy ranges from 80% to 85%, showing room for improvement is left. By providing this dataset and a standardized evaluation protocol to the scientific community, we hope to gather researchers in both the medical and the machine learning field to advance toward this clinical application.
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A survey of the recent architectures of deep convolutional neural networks
Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09825-6 · Cited by 3,137
A Survey of Deep Active Learning
Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Gupta, B. B., Chen, X., & Wang, X. (2022) · ACM Computing Surveys · doi:10.1145/3472291 · Cited by 1,169
CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope
Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., & Ghayvat, H. (2021) · Electronics · doi:10.3390/electronics10202470 · Cited by 890
Khan, S., Islam, N., Jan, Z., Ud Din, I., & Rodrigues, J. J. P. C. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.03.022 · Cited by 785
Deep neural network models for computational histopathology: A survey
Srinidhi, C. L., Ciga, O., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101813 · Cited by 749
Metadata
- Authors
- Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2016-07
- Volume
- 63
- Issue
- 7
- Pages
- 1455-1462
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2344480160
- Cited by
- 1,832
- References
- 6
- FWCI
- 34.10
- Type
- journal article
- Language
- en
- Open access
- closed
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- Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2016). A Dataset for Breast Cancer Histopathological Image Classification. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2015.2496264
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