In the diagnosis of preinvasive breast cancer, some of the intraductal proliferations pose a special challenge. The continuum of intraductal breast lesions includes the usual ductal hyperplasia (UDH), atypical ductal hyperplasia (ADH), and ductal carcinoma in situ (DCIS). The current standard of care is to perform percutaneous needle biopsies for diagnosis of palpable and image-detected breast abnormalities. UDH is considered benign and patients diagnosed UDH undergo routine follow-up, whereas ADH and DCIS are considered actionable and patients diagnosed with these two subtypes get additional surgical procedures. About 250 000 new cases of intraductal breast lesions are diagnosed every year. A conservative estimate would suggest that at least 50% of these patients are needlessly undergoing unnecessary surgeries. Thus, improvement in the diagnostic reproducibility and accuracy is critically important for effective clinical management of these patients. In this study, a prototype system for automatically classifying breast microscopic tissues to distinguish between UDH and actionable subtypes (ADH and DCIS) is introduced. This system automatically evaluates digitized slides of tissues for certain cytological criteria and classifies the tissues based on the quantitative features derived from the images. The system is trained using a total of 327 regions of interest (ROIs) collected across 62 patient cases and tested with a sequestered set of 149 ROIs collected across 33 patient cases. An overall accuracy of 87.9% is achieved on the entire test data. The test accuracy of 84.6% is obtained with borderline cases (26 of the 33 test cases) only, when compared against the diagnostic accuracies of nine pathologists on the same set (81.2% average), indicates that the system is highly competitive with the expert pathologists as a stand-alone diagnostic tool and has a great potential in improving diagnostic accuracy and reproducibility when used as a “second reader” in conjunction with the pathologists.

This paper presents an unsupervised hyperspectral image segmentation with a new subtractive-clustering-based similarity segmentation and a novel cluster validation method using one-class support vector (SV) machine (OC-SVM). An estimation of the correct number of clusters is an important task in hyperspectral image segmentation. The proposed cluster validity measure is based on the power of spectral discrimination (PWSD) measure and utilizes the advantage of the inherited cluster contour definition feature of OC-SVM. Hence, this novel cluster validity method is referred to as SV-PWSD. SVs found by OC-SVM are located at the minimum distance to the hyperplane in the feature space and at the arbitrarily shaped cluster contours in the input space. SV-PWSD guides the segmentation/clustering process to find the optimal number of clusters in hyperspectral data. Because of the high computational load of subtractive clustering and OC-SVM, a subset of the image (only ground-truth data) is initially used in the clustering and validation phases. Then, it is proposed to use K-nearest neighbor classification, with the already clustered subset being used as training data, to project the initial clustering results onto the entire data set.

Breast cancer is the second leading cause of cancer death in women according to World Health Organization (WHO). Development of computer aided diagnostic (CAD) systems has great importance as a secondary reader systems for a correct diagnosis and treatment process. In this paper, a deep learning based feature extraction method by convolutional neural network (CNN) is proposed for automated mitosis detection for cancer diagnosis and grading by histopathological images. The proposed framework is tested on the MITOS data set provided for a contest on mitosis detection in breast cancer histological images released for research purposes in International Conference on Pattern Recognition (ICPR'2014). By using provided histopathological images, cellular structures are initially found by combined clustering based segmentation and blob analysis after preprocessing step. Then, obtained cellular image patches are cropped automatically from the histopathological images for feature extraction stage. CNN, which is a prominent deep learning method on image processing tasks, is utilized for extracting discriminative features. Due to the high dimensional output of the CNN, combination of PCA and LDA dimension reduction methods are performed respectively for regularization and dimension reduction process. Afterwards, a robust kernel based classifier, support vector machine (SVM), is used for final classification of mitotic and non-mitotic cells. The test results on MITOS data set prove that the proposed framework achieved promising results for mitosis detection on histopathological images.

This letter presents unsupervised hyperspectral-image classification based on fuzzy-clustering algorithms that spatially exploit membership relations. Not only is the conventional fuzzy c-means approach used to demonstrate the advantage of using membership relations but also Gustafson-Kessel clustering, which uses an adaptive distance norm, is, for the first time, used for the segmentation of hyperspectral images. A novel approach to include spatial information in the segmentation process is achieved by making use of spatial relations of fuzzy-membership functions among neighbor pixels. Two- and three-dimensional Gaussian filtering of fuzzy-membership degrees is utilized for this purpose. A novel phase-correlation-based similarity measure is used to further enhance the performance of the proposed approach by taking spatial relations into account for pixels with similar spectral characteristics only. It is shown that the proposed approach provides superior clustering performance for hyperspectral images.