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Adding geodesic information and stochastic patch-wise image prediction for small dataset learning
- Source :
- Neurocomputing, Neurocomputing, Elsevier, 2021, 456, pp.481-491. ⟨10.1016/j.neucom.2021.01.108⟩, Neurocomputing, Elsevier, 2021
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- International audience; Most recent methods of image augmentation and prediction are building upon the deep learning paradigm. A careful preparation of the image dataset and the choice of a suitable network architecture are crucial steps to assess the desired image features and, thence, achieve accurate predictions. We first propose to help the learning process by adding structural information with specific distance transform to the input image data. To handle cases with limited number of training samples, we propose a patch-based procedure with a stratified sampling method at inference. We validate our approaches on two image datasets, corresponding to two different tasks. The ability of our method to segment and predict images is investigated through the ISBI 2012 segmentation challenge dataset and generated electric field masks, respectively. The obtained results are evaluated using appropriate metrics: VRand for image segmentation and SSIM, UIQ and PSNR for image prediction. The proposed techniques demonstrate that the established framework is a reliable estimation method that could be used for a wide range of applications.
- Subjects :
- 0209 industrial biotechnology
Computer science
Cognitive Neuroscience
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
stratified sampling
distance transform
patch-wise segmentation
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Image (mathematics)
020901 industrial engineering & automation
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Segmentation
business.industry
Deep learning
image augmentation
deep learning
Pattern recognition
Image segmentation
Computer Science Applications
Range (mathematics)
Feature (computer vision)
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
020201 artificial intelligence & image processing
Artificial intelligence
business
Distance transform
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 456
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi.dedup.....066ea12512be9eb6f5f35ec0a51a36bb