Author: Amine Amyar; Romain Modzelewski; Su Ruan
Title: Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation Document date: 2020_4_21
ID: hiac6ur7_35
Snippet: We conducted three experiments to evaluate our model. Experiment 1: The first experiment consisted of tuning the hyperparameters and add/remove a task to find the best model using only the training dataset. Several models were developed by combining the tasks 2 by 2 and the 3 tasks with different resolutions of images (512 x 512 and 256 x 256). The combination of the first task and the second one is only to evaluate segmentation results, since it.....
Document: We conducted three experiments to evaluate our model. Experiment 1: The first experiment consisted of tuning the hyperparameters and add/remove a task to find the best model using only the training dataset. Several models were developed by combining the tasks 2 by 2 and the 3 tasks with different resolutions of images (512 x 512 and 256 x 256). The combination of the first task and the second one is only to evaluate segmentation results, since it is for image reconstruction and infection segmentation, while the peer T1 and T3 is for classification. Experiment 2: The second experiment consisted of comparing our model with state of the art method U-NET to compare the performance on the segmentation task. Two U-NET with different resolutions were trained: 512 x 512 and 256 x 256. In Fig 6 a
Search related documents:
Co phrase search for related documents- art method and infection segmentation: 1, 2, 3, 4, 5
- art method and model evaluate: 1
- art method and segmentation result: 1
- art method and segmentation task: 1, 2, 3, 4
- art method state and good model: 1
- art method state and infection segmentation: 1, 2, 3, 4, 5
- art method state and model evaluate: 1
- art method state and segmentation task: 1, 2, 3, 4
- different resolution and good model: 1
- good model and image reconstruction: 1
- good model and model compare: 1, 2, 3
- good model and model evaluate: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
- infection segmentation and segmentation result: 1
- infection segmentation and segmentation task: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
- infection segmentation and segmentation task performance: 1
- model compare and second experiment: 1
Co phrase search for related documents, hyperlinks ordered by date