Khryashchev et al., 2019 - Google Patents
Using convolutional neural networks in the problem of cell nuclei segmentation on histological imagesKhryashchev et al., 2019
- Document ID
- 12823922154677831744
- Author
- Khryashchev V
- Lebedev A
- Stepanova O
- Srednyakova A
- Publication year
- Publication venue
- International Conference on Information Technologies
External Links
Snippet
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 …
- 230000001537 neural 0 title abstract description 35
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Oskal et al. | A U-net based approach to epidermal tissue segmentation in whole slide histopathological images | |
Cai et al. | Pancreas segmentation in MRI using graph-based decision fusion on convolutional neural networks | |
Salvi et al. | Multi-tissue and multi-scale approach for nuclei segmentation in H&E stained images | |
Wang et al. | A deep learning approach for semantic segmentation in histology tissue images | |
Mahbod et al. | Breast cancer histological image classification using fine-tuned deep network fusion | |
Zhang et al. | Sam-path: A segment anything model for semantic segmentation in digital pathology | |
Soltaninejad et al. | Efficient MRI brain tumor segmentation using multi-resolution encoder-decoder networks | |
Khryashchev et al. | Using convolutional neural networks in the problem of cell nuclei segmentation on histological images | |
Wu et al. | FF-CNN: An efficient deep neural network for mitosis detection in breast cancer histological images | |
Pal et al. | Capsdemm: capsule network for detection of munro’s microabscess in skin biopsy images | |
Scheurer et al. | Semantic segmentation of histopathological slides for the classification of cutaneous lymphoma and eczema | |
Huang et al. | Ca 2.5-net nuclei segmentation framework with a microscopy cell benchmark collection | |
Hosseini et al. | On transferability of histological tissue labels in computational pathology | |
Sun et al. | GSplit LBI: Taming the procedural bias in neuroimaging for disease prediction | |
Nguyen et al. | A visually explainable learning system for skin lesion detection using multiscale input with attention U-Net | |
Li et al. | A cascaded 3d segmentation model for renal enhanced CT images | |
Gou et al. | Artificial intelligence multiprocessing scheme for pathology images based on transformer for nuclei segmentation | |
Ametefe et al. | Automatic classification and segmentation of blast cells using deep transfer learning and active contours | |
Abrol et al. | An automated segmentation of leukocytes using modified watershed algorithm on peripheral blood smear images | |
Decencière et al. | Dealing with topological information within a fully convolutional neural network | |
Mukundan | A robust algorithm for automated her2 scoring in breast cancer histology slides using characteristic curves | |
Singh et al. | A study of nuclei classification methods in histopathological images | |
Zhai et al. | Deep neural network guided by attention mechanism for segmentation of liver pathology image | |
Tripathi et al. | An object aware hybrid U-net for breast tumour annotation | |
Yan et al. | Hsdet: A representative sampling based object detector in cervical cancer cell images |