We address the problem of analyzing histopathology images, using computational tools, to automatically find patterns related with pathology signatures associated with healthy and abnormal tissues, which are fundamental support for cancer diagnosis. Computational pathology is a relatively recent research area devoted to providing accurate and efficient computational methods to support quantitative detection, diagnosis, and prognosis in pathology. We present several computational and machine learning methods for efficient and effective automatic histopathology image analysis exploiting histopathology image databases for different digital pathology tasks including tumor and tissue detection, location and quantification in several cancer types.
Victor Hugo Contreras Ordoñez
Juan Sebastián Lara Ramírez
Johan David Rodriguez Portela
Arevalo, J., Cruz-Roa, A., Arias, V., Romero, E., & Gonzalez, F. A. (2015). An unsupervised feature learning framework for basal cell carcinoma image analysis. Artificial intelligence in medicine, Vol 64, Issue 2, pp. 131–145. ISSN 0933-3657.doi:10.1016/j.artmed.2015.04. 004. (PDF).
Cruz-Roa, A., Gilmore, H., Basavanhally, A., Feldman, M., Ganesan, S., Shih, N., … & González, F. (2018). High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection. PloS one, 13(5), e0196828. doi: 10.1371/journal.pone.0196828. (PDF).
Cruz-Roa, A, Díaz, G., Romero, E., & Gonzalez, F. A. (2011). Automatic annotation of histopathological images using a latent topic model based on non-negative matrix factorization. Journal of Pathology Informatics, Vol 2. No. 4. doi:10.4103/2153-3539.92031.
Cruz-Roa, A., Gilmore, H., Basavanhally, A., Feldman, M., Ganesan, S., NC Shih, N., Tomaszewski, J., González, F.A., Madabhushi, A. Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent.in Journal Scientific Reports (Nature Publishing Group). Vol. 7, Issue 46450, pp. 1-14. http://dx.doi.org/10.1038/srep46450. (PDF)