Schulz et al., 2019 - Google Patents
Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasetsSchulz et al., 2019
View PDF- Document ID
- 7803843790788736885
- Author
- Schulz M
- Yeo B
- Vogelstein J
- Mourao-Miranada J
- Kather J
- Kording K
- Richards B
- Bzdok D
- Publication year
- Publication venue
- BioRxiv
External Links
Snippet
In recent years, deep learning has unlocked unprecedented success in various domains, especially in image, text, and speech processing. These breakthroughs may hold promise for neuroscience and especially for brain-imaging investigators who start to analyze …
- 210000004556 Brain 0 title abstract description 70
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
- G06K9/6217—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
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- 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
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Schulz et al. | Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets | |
Schulz et al. | Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasets | |
Eslami et al. | Machine learning methods for diagnosing autism spectrum disorder and attention-deficit/hyperactivity disorder using functional and structural MRI: a survey | |
Gupta et al. | Deep learning in image cytometry: a review | |
Giovannucci et al. | CaImAn an open source tool for scalable calcium imaging data analysis | |
Bzdok et al. | Towards algorithmic analytics for large-scale datasets | |
Lima et al. | A comprehensive survey on the detection, classification, and challenges of neurological disorders | |
Veronese et al. | Machine learning approaches: from theory to application in schizophrenia | |
Rakhimberdina et al. | Population graph-based multi-model ensemble method for diagnosing autism spectrum disorder | |
Naik et al. | Denouements of machine learning and multimodal diagnostic classification of Alzheimer’s disease | |
Khajehnejad et al. | Alzheimer’s disease early diagnosis using manifold-based semi-supervised learning | |
Balamurugan et al. | Alzheimer’s disease diagnosis by using dimensionality reduction based on knn classifier | |
Raja et al. | Conditional Generative Adversarial Network Approach for Autism Prediction. | |
Pominova et al. | Fader networks for domain adaptation on fMRI: ABIDE-II study | |
Behara et al. | Skin lesion synthesis and classification using an improved DCGAN classifier | |
Ayman et al. | Epileptic patient activity recognition system using extreme learning machine method | |
Dwivedi et al. | Unraveling representations in scene-selective brain regions using scene-parsing deep neural networks | |
Tuvshinjargal et al. | VGG-C transform model with batch normalization to predict Alzheimer’s disease through MRI dataset | |
Almalki et al. | Robust Gaussian and nonlinear hybrid invariant clustered features aided approach for speeded brain tumor diagnosis | |
Bahrami et al. | Using low-dimensional manifolds to map relationships between dynamic brain networks | |
Acharjya et al. | Deep learning in data analytics | |
Sheikh et al. | Unsupervised learning based on multiple descriptors for WSIs diagnosis | |
Lou et al. | Predicting radiologists' gaze with computational saliency models in mammogram reading | |
Nayak et al. | Non-linear cellular automata based edge detector for optical character images | |
Gassner et al. | Saliency-enhanced content-based image retrieval for diagnosis support in dermatology consultation: Reader study |