Book ChapterDOI
Using Convolutional Neural Networks in the Problem of Cell Nuclei Segmentation on Histological Images
Vladimir Khryashchev,Anton Lebedev,Olga Stepanova,Anastasiya Srednyakova +3 more
- pp 149-161
Reads0
Chats0
TLDR
The research shows that the fast algorithm based on neural network U-Net can be successfully used for the histological image segmentation in real medical practice, which is confirmed by the high level of similarity of the obtained markup with the expert one.Abstract:
Computer-aided diagnostics of cancer pathologies based on histological image segmentation is a promising area in the field of computer vision and machine learning. To date, the successes of neural networks in image segmentation in a number of tasks are comparable to human results and can even exceed them. The paper presents a fast algorithm of histological image segmentation based on the convolutional neural network U-Net. Using this approach allows to get better results in the tasks of medical image segmentation. The developed algorithm based on neural network AlexNet was used for the creation of the automatic markup of the histological image database. The neural network algorithms were trained and tested on the NVIDIA DGX-1 supercomputer using histological images. The results of the research show that the fast algorithm based on neural network U-Net can be successfully used for the histological image segmentation in real medical practice, which is confirmed by the high level of similarity of the obtained markup with the expert one.read more
Citations
More filters
Posted Content
Ranger: Boosting Error Resilience of Deep Neural Networks through Range Restriction.
TL;DR: Ranger is proposed, an automated technique to selectively restrict the ranges of values in particular DNN layers, which can dampen the large deviations typically caused by critical faults to smaller ones and achieve significant resilience boosting without degrading the accuracy of the model, and incurring negligible overheads.
Proceedings ArticleDOI
Automatic Identification of Appendiceal Orifice on Colonoscopy Images Using Deep Neural Network
Anton Lebedev,Vladimir Khryashchev,Evgeniya Kazina,Anastasia Zhuravleva,Sergey V. Kashin,Dmitry Zavyalov +5 more
TL;DR: The results of testing the recognition algorithm of the cecum achievement in colonoscopy video of the colon mucosa are presented and the introduction of such a system in medical practice will partially automate the analysis of video data, which will subsequently lead to a decrease in the number of subjective medical errors during Colonoscopy.
Book ChapterDOI
Deep Neural Networks Application in Models with Complex Technological Objects
TL;DR: A method for creation of computer models in complex multiply connected technological objects based on the application of machine learning methods is described and hierarchical neural network structure integrated into cyber-physical systems of control is developed.
Proceedings ArticleDOI
Error Resilient Machine Learning for Safety-Critical Systems: Position Paper
TL;DR: The resilience of safety-critical ML applications to soft errors is experimentally assessed, and BinFI, a fault injection approach that efficiently injects critical faults that are highly likely to result in safety violations, is proposed, based on the unique properties of DNNs.
Book ChapterDOI
Histological Images Segmentation by Convolutional Neural Network with Morphological Post-filtration
TL;DR: A quality of histological image segmentation developed by the Morphological filtering algorithm exceeds the similar performance for the algorithm without using morphological filtering, which allows the morphological filter to be recommended as a means of additional image processing at the output of the neural network algorithm in real medical practice.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Posted Content
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Journal ArticleDOI
Histopathological Image Analysis: A Review
TL;DR: The recent state of the art CAD technology for digitized histopathology is reviewed and the development and application of novel image analysis technology for a few specific histopathological related problems being pursued in the United States and Europe are described.
Journal ArticleDOI
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases.
TL;DR: This paper investigates concepts through seven unique DP tasks as use cases to elucidate techniques needed to produce comparable, and in many cases, superior to results from the state-of-the-art hand-crafted feature-based classification approaches.