Benny et al., 2021 - Google Patents
Semantic segmentation in immunohistochemistry breast cancer image using deep learningBenny et al., 2021
- Document ID
- 1940000270414988903
- Author
- Benny S
- Varma S
- Publication year
- Publication venue
- 2021 International Conference on Advances in Computing, Communication, and Control (ICAC3)
External Links
Snippet
Cancer is affecting many people's lives. Uncontrollable growth of cells causes cancer. Thus, there is a need to find accurate results for the treatment of a patient by using the proper computation method. Immunohistochemistry (IHC) image is the study of stained cancerous …
- 230000011218 segmentation 0 title abstract description 33
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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30024—Cell structures in vitro; Tissue sections in vitro
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- 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
- G06T2207/10056—Microscopic image
-
- 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
- G06K9/00147—Matching; Classification
-
- 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
- G06K9/0014—Pre-processing, e.g. image segmentation ; Feature extraction
-
- 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
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US12073948B1 (en) | Systems and methods for training a model to predict survival time for a patient | |
JP7422235B2 (en) | Non-tumor segmentation to aid tumor detection and analysis | |
JP7695256B2 (en) | A Federated Learning System for Training Machine Learning Algorithms and Maintaining Patient Privacy | |
JP7427080B2 (en) | Weakly supervised multitask learning for cell detection and segmentation | |
US20230186659A1 (en) | Machine learning models for cell localization and classification learned using repel coding | |
US20240079116A1 (en) | Automated segmentation of artifacts in histopathology images | |
Nofallah et al. | Machine learning techniques for mitoses classification | |
CN113261012A (en) | Method, device and system for processing image | |
JP2025500431A (en) | Adversarial Robustness of Deep Learning Models in Digital Pathology | |
Salsabili et al. | Fully automated estimation of the mean linear intercept in histopathology images of mouse lung tissue | |
Benny et al. | Semantic segmentation in immunohistochemistry breast cancer image using deep learning | |
Zurek et al. | Immunohistochemistry annotations enhance AI identification of lymphocytes and neutrophils in digitized H&E slides from inflammatory bowel disease | |
US9785848B2 (en) | Automated staining and segmentation quality control | |
Guerrero et al. | Improvements in lymphocytes detection using deep learning with a preprocessing stage | |
CN117425912A (en) | Conversion of histochemical staining images to synthetic Immunohistochemical (IHC) images | |
Bueno-Crespo et al. | Diagnosis of cervical cancer using a deep learning explainable fusion model | |
Lin Huang | U-Net vs HoVer-Net: A Comparative Study of Deep Learning Models for Cell Nuclei Segmentation and Classification in Breast Cancer Diagnosis | |
Xu et al. | Detection and Classification of Breast Cancer Metastates Based on U-Net | |
Sabban et al. | Segmenting Glandular Biopsy Images Using the Separate Merged Objects Algorithm | |
Midden et al. | Deep learning-based histopathologic segmentation of peritubular capillaries in kidney transplant biopsies. | |
Han et al. | Glomerular Microscopic Image Segmentation Based on Convolutional Neural Network | |
Van Eycke et al. | Automatic segmentation of glandular epithelium in colorectal tissue images using Deep Learning in order to compartmentalize IHC biomarker quantification |