You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
19 June 2017Experiments on automatic classification of tissue malignancy in the field of digital pathology
Automated analysis of histological images helps diagnose and further classify breast cancer. Totally automated approaches can be used to pinpoint images for further analysis by the medical doctor. But tissue images are especially challenging for either manual or automated approaches, due to mixed patterns and textures, where malignant regions are sometimes difficult to detect unless they are in very advanced stages. Some of the major challenges are related to irregular and very diffuse patterns, as well as difficulty to define winning features and classifier models. Although it is also hard to segment correctly into regions, due to the diffuse nature, it is still crucial to take low-level features over individualized regions instead of the whole image, and to select those with the best outcomes. In this paper we report on our experiments building a region classifier with a simple subspace division and a feature selection model that improves results over image-wide and/or limited feature sets. Experimental results show modest accuracy for a set of classifiers applied over the whole image, while the conjunction of image division, per-region low-level extraction of features and selection of features, together with the use of a neural network classifier achieved the best levels of accuracy for the dataset and settings we used in the experiments. Future work involves deep learning techniques, adding structures semantics and embedding the approach as a tumor finding helper in a practical Medical Imaging Application.
J. Pereira,R. Barata, andPedro Furtado
"Experiments on automatic classification of tissue malignancy in the field of digital pathology", Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 1044312 (19 June 2017); https://doi.org/10.1117/12.2280294
The alert did not successfully save. Please try again later.
J. Pereira, R. Barata, Pedro Furtado, "Experiments on automatic classification of tissue malignancy in the field of digital pathology," Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 1044312 (19 June 2017); https://doi.org/10.1117/12.2280294