Erakusten 1 - 13 emaitzak -- 13 bilaketa honetara 'Mehmet Zahid Genc (22683526)', Bilaketaren denbora: 0,01s Findu emaitzak
  1. 1
    A sample of DSDS spleen dataset: (a) Raw image; (b) Ground truth (spleen region is indicated as green color); (c) 3D display.

    A sample of DSDS spleen dataset: (a) Raw image; (b) Ground truth (spleen region is indicated as green color); (c) 3D display. nork Mehmet Zahid Genc (22683526)

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  2. 2
    The architecture of the SVNC-Net.

    The architecture of the SVNC-Net. nork Mehmet Zahid Genc (22683526)

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  3. 3
    Impacts of pruning and quantization on the model performances.

    Impacts of pruning and quantization on the model performances. nork Mehmet Zahid Genc (22683526)

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  4. 4
    Qualitative comparison of spleen segmentation results on the MSD dataset: (a) Input CT slice, (b) Ground Truth (red boundary), (c) UPerNet prediction, (d) EMANet prediction, (e) CCNet prediction, (f) SegNet prediction, (g) ShuffleNet prediction, (h) SVNC-Net prediction.

    Qualitative comparison of spleen segmentation results on the MSD dataset: (a) Input CT slice, (b) Ground Truth (red boundary), (c) UPerNet prediction, (d) EMANet prediction, (e) CC... nork Mehmet Zahid Genc (22683526)

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  5. 5
    Segmentation accuracy of the models.

    Segmentation accuracy of the models. nork Mehmet Zahid Genc (22683526)

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  6. 6
    The process of standard convolution and depthwise separable convolution.

    The process of standard convolution and depthwise separable convolution. nork Mehmet Zahid Genc (22683526)

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  7. 7
    Training and implementation details.

    Training and implementation details. nork Mehmet Zahid Genc (22683526)

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  8. 8
    A sample of MSD spleen dataset: (a) Raw image; (b) Ground truth (spleen region is indicated as green color); (c) 3D display.

    A sample of MSD spleen dataset: (a) Raw image; (b) Ground truth (spleen region is indicated as green color); (c) 3D display. nork Mehmet Zahid Genc (22683526)

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  9. 9
    Qualitative comparison of spleen segmentation results on the DSDS: (a) Input CT slice, (b) Ground Truth (red boundary), (c) UPerNet prediction, (d) EMANet prediction, (e) CCNet prediction, (f) SegNet prediction, (g) ShuffleNet prediction, (h) SVNC-Net prediction.

    Qualitative comparison of spleen segmentation results on the DSDS: (a) Input CT slice, (b) Ground Truth (red boundary), (c) UPerNet prediction, (d) EMANet prediction, (e) CCNet pre... nork Mehmet Zahid Genc (22683526)

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  10. 10
    Fold assignments for each evaluation round.

    Fold assignments for each evaluation round. nork Mehmet Zahid Genc (22683526)

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  11. 11
    Studies on the use of 2D CNN-based models for spleen segmentation from 3D CT images.

    Studies on the use of 2D CNN-based models for spleen segmentation from 3D CT images. nork Mehmet Zahid Genc (22683526)

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  12. 12
    Computational efficiency of the models.

    Computational efficiency of the models. nork Mehmet Zahid Genc (22683526)

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  13. 13
    Summary of key architectural modifications and hyperparameter adjustments from the U-Net to SVNC-Net.

    Summary of key architectural modifications and hyperparameter adjustments from the U-Net to SVNC-Net. nork Mehmet Zahid Genc (22683526)

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