Selected article for: "classification segmentation and dice coefficient"
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_42
    Snippet: The main results of the three experiments are shown in Table 2. Metrics include: dice coefficient, accuracy, sensibility, specificity and the area under the ROC curve. Experiment 1: As shown in Table 2 Table 2 , the best result for image segmentation was obtained using our method with a dice coef of 78.52% versus 69.09% and 67.14% using U-NET with 256 x 256 and 512 x 512 resolutions respectively. The combination of the reconstruction, segmentatio.....
    Document: The main results of the three experiments are shown in Table 2. Metrics include: dice coefficient, accuracy, sensibility, specificity and the area under the ROC curve. Experiment 1: As shown in Table 2 Table 2 , the best result for image segmentation was obtained using our method with a dice coef of 78.52% versus 69.09% and 67.14% using U-NET with 256 x 256 and 512 x 512 resolutions respectively. The combination of the reconstruction, segmentation and classification results in a higher accuracy to detect infection regions, compared to the use of the U-NET model alone.

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