“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Digital image analysis in breast pathology-from image processing techniques to artificial intelligence.

Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.

https://www.mdpi.com/1424-8220/19/23/5170/pdf

A Survey on Machine-Learning Techniques for UAV-Based Communications

Decision Forests For Computer Vision And Medical Image Analysis

We propose an improved random forest classifier that performs classification with minimum number of trees. The proposed method iteratively removes some unimportant features. Based on the number of important and unimportant features, we formulate a novel theoretical upper limit on the number of trees to be added to the forest to ensure improvement in classification accuracy. Our algorithm converges with a reduced but important set of features. We prove that further addition of trees or further reduction of features does not improve classification performance. The efficacy of the proposed approach is demonstrated through experiments on benchmark datasets. We further use the proposed classifier to detect mitotic nuclei in the histopathological datasets of breast tissues. We also apply our method on the industrial dataset of dual phase steel microstructures to classify different phases. Results of our method on different datasets show significant reduction in average classification error compared to a number of competing methods.

Improved Random Forest for Classification.

Histopathological grading of cancer not only offers an insight to the patients’ prognosis but also helps in making individual treatment plans. Mitosis counts in histopathological slides play a crucial role for invasive breast cancer grading using the Nottingham grading system. Pathologists perform this grading by manual examinations of a few thousand images for each patient. Hence, finding the mitotic figures from these images is a tedious job and also prone to observer variability due to variations in the appearances of the mitotic cells. We propose a fast and accurate approach for automatic mitosis detection from histopathological images. We employ area morphological scale space for cell segmentation. The scale space is constructed in a novel manner by restricting the scales with the maximization of relative-entropy between the cells and the background. This results in precise cell segmentation. The segmented cells are classified in mitotic and non-mitotic category using the random forest classifier. Experiments show at least 12% improvement in $F_{1}$ score on more than 450 histopathological images at $40\times $ magnification.

Mitosis Detection for Invasive Breast Cancer Grading in Histopathological Images

We propose a fast and accurate method for counting the mitotic figures from histopathological slides using regenerative random forest. Our method performs automatic feature selection in an integrated manner with classification. The proposed random forest assigns a weight to each feature (dimension) of the feature vector in a novel manner based on the importance of the feature (dimension). The forest also assigns a misclassification-based penalty term to each tree in the forest. The trees are then regenerated to make a new population of trees (new forest) and only the more important features survive in the new forest. The feature vector is constructed from domain knowledge using the intensity features of nucleus, features of nuclear membrane and features of the possible stroma region surrounding the cell. The use of domain knowledge improves the classification performance. Experiments show at least 4% improvement in F-measure with an improvement in time complexity on the MITOS dataset from ICPR 2012 grand challenge.

/pdf/regenerative-random-forest-with-automatic-feature-selection-1l0zkre3ce.pdf

Regenerative Random Forest with Automatic Feature Selection to Detect Mitosis in Histopathological Breast Cancer Images

Discriminative ensemble learning for few-shot chest x-ray diagnosis.

Grading of cancer offers insight to the occurrence and progress of the disease. The course of treatment is planned depending on the grade of cancer. Segmentation of the glandular structure of tissue is a prerequisite for grading of colon, prostate and breast cancers. Manual segmentation method is time-consuming and suffers from the curse of observer bias. We propose an automated solution for gland segmentation from hematoxylin & eosin (H&E) stained histology images. Our method relies on the biological cue rather than gland specific signatures that may vary across the slides. We construct a novel informative morphological scale space for gland segmentation. The scale space uses the entropy of the connected components in a novel manner to prevent over segmentation of objects. Our solution is fast, accurate and applicable in a clinical setup. Experiments show an average F1 score of 0.68 for 85 histology images in 20x magnification. We obtain ∼ 30% improvement in F1 score compared to the area morphological scale space method.

Angshuman Paul

Papers

Improved Random Forest for Classification.

Mitosis Detection for Invasive Breast Cancer Grading in Histopathological Images

Regenerative Random Forest with Automatic Feature Selection to Detect Mitosis in Histopathological Breast Cancer Images

Discriminative ensemble learning for few-shot chest x-ray diagnosis.

Gland segmentation from histology images using informative morphological scale space