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

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

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

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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... Tekijä Mehmet Zahid Genc (22683526)

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

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

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

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

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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... Tekijä Mehmet Zahid Genc (22683526)

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

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

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

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

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