Nothing Special   »   [go: up one dir, main page]

Jung et al., 2022 - Google Patents

Uncertainty estimation for multi-view data: The power of seeing the whole picture

Jung et al., 2022

View PDF
Document ID
7538931144727949240
Author
Jung M
Zhao H
Dipnall J
Gabbe B
Du L
Publication year
Publication venue
Advances in Neural Information Processing Systems

External Links

Snippet

Uncertainty estimation is essential to make neural networks trustworthy in real-world applications. Extensive research efforts have been made to quantify and reduce predictive uncertainty. However, most existing works are designed for unimodal data, whereas multi …
Continue reading at proceedings.neurips.cc (PDF) (other versions)

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
    • G06K9/62—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
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • 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
    • 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
    • G06K9/36—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
    • 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
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • 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
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/00771—Recognising scenes under surveillance, e.g. with Markovian modelling of scene activity
    • 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
    • G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • 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
    • 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
    • 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
Mehrtash et al. Confidence calibration and predictive uncertainty estimation for deep medical image segmentation
Noor et al. Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia
Krebs et al. Unsupervised probabilistic deformation modeling for robust diffeomorphic registration
US20230162049A1 (en) Artificial intelligence (ai) method for cleaning data for training ai models
Li et al. Multiple-instance learning as a classifier combining problem
Lee et al. Using deep CNN with data permutation scheme for classification of Alzheimer's disease in structural magnetic resonance imaging (sMRI)
Bi et al. An improved multi-task learning approach with applications in medical diagnosis
Kalane et al. Automatic detection of COVID-19 disease using U-Net architecture based fully convolutional network
US11790492B1 (en) Method of and system for customized image denoising with model interpretations
Forouzannezhad et al. A Gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging
Jung et al. Uncertainty estimation for multi-view data: The power of seeing the whole picture
Gnanaguru et al. Thorough Analysis of Deep Learning Methods for Diagnosis of COVID-19 CT Images
Zamani et al. Towards Applicability of Information Communication Technologies in Automated Disease Detection.
JP2017102906A (en) Information processing apparatus, information processing method, and program
Ragupathy et al. A deep learning model integrating convolution neural network and multiple kernel K means clustering for segmenting brain tumor in magnetic resonance images
Prasad et al. Lung cancer detection and classification using deep neural network based on hybrid metaheuristic algorithm
Yazdani et al. Diagnosis of COVID‐19 Disease in Chest CT‐Scan Images Based on Combination of Low‐Level Texture Analysis and MobileNetV2 Features
Larrazabal et al. Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation
Kesuma ELREI: Ensemble Learning of ResNet, EfficientNet, and Inception-v3 for Lung Disease Classification based on Chest X-Ray Image.
Reimers et al. Towards learning an unbiased classifier from biased data via conditional adversarial debiasing
Larrazabal et al. Maximum entropy on erroneous predictions: Improving model calibration for medical image segmentation
Gao et al. Multi-objective fuzzy clustering for synthetic aperture radar imagery
Dommeti et al. Repercussions of Incorporating Filters in CNN Model to Boost the Diagnostic Ability of SARS-CoV-2 Virus Using Chest Computed Tomography Scans
Si et al. Artificial neural network based lesion segmentation of brain MRI
Fan et al. Flexible visual recognition by evidential modeling of confusion and ignorance