Raipuria et al., 2022 - Google Patents
Stain-aglr: Stain agnostic learning for computational histopathology using domain consistency and stain regeneration lossRaipuria et al., 2022
- Document ID
- 7338574101796351024
- Author
- Raipuria G
- Shrivastava A
- Singhal N
- Publication year
- Publication venue
- MICCAI Workshop on Domain Adaptation and Representation Transfer
External Links
Snippet
Stain color variations between Whole Slide Images (WSIs) is a key challenge in the application of Computational Histopathology. Deep learning-based algorithms are susceptible to domain shift and degrade in performance on the WSIs captured from a …
- 230000008929 regeneration 0 title description 18
Classifications
-
- 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
- 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
- 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
- G06T2207/10024—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
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- 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
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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
-
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Li et al. | Improving semantic segmentation via decoupled body and edge supervision | |
Hörst et al. | Cellvit: Vision transformers for precise cell segmentation and classification | |
Heinrich et al. | Synaptic cleft segmentation in non-isotropic volume electron microscopy of the complete drosophila brain | |
Veeling et al. | Rotation equivariant CNNs for digital pathology | |
Shimoda et al. | Distinct class-specific saliency maps for weakly supervised semantic segmentation | |
Janowczyk et al. | Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases | |
BenTaieb et al. | Predicting cancer with a recurrent visual attention model for histopathology images | |
Thuy et al. | Fusing of deep learning, transfer learning and gan for breast cancer histopathological image classification | |
Morelli et al. | Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet | |
Zidan et al. | SwinCup: Cascaded swin transformer for histopathological structures segmentation in colorectal cancer | |
Chrysos et al. | Rocgan: Robust conditional gan | |
Öztürk et al. | Cell‐type based semantic segmentation of histopathological images using deep convolutional neural networks | |
Sun et al. | An information theoretic approach for attention-driven face forgery detection | |
Marini et al. | Multi_Scale_Tools: a python library to exploit multi-scale whole slide images | |
Zhang et al. | Attention-challenging multiple instance learning for whole slide image classification | |
Hosseini et al. | On transferability of histological tissue labels in computational pathology | |
Scalbert et al. | Test-time image-to-image translation ensembling improves out-of-distribution generalization in histopathology | |
Doan et al. | Gradmix for nuclei segmentation and classification in imbalanced pathology image datasets | |
Yan et al. | LocMix: local saliency-based data augmentation for image classification | |
Matskevych et al. | From shallow to deep: exploiting feature-based classifiers for domain adaptation in semantic segmentation | |
Rauf et al. | Lymphocyte detection for cancer analysis using a novel fusion block based channel boosted CNN | |
Raipuria et al. | Stain-aglr: Stain agnostic learning for computational histopathology using domain consistency and stain regeneration loss | |
Pina et al. | Cell-DETR: Efficient cell detection and classification in WSIs with transformers | |
Ye et al. | A multi-attribute controllable generative model for histopathology image synthesis | |
Nateghi et al. | Two-step domain adaptation for mitotic cell detection in histopathology images |