Comparison of prediction performance with previously reported metrics. od Raiki Yoshimura (22676573)

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Stratification and characterization of groups showing different graft survival: (A) UMAP visualization of the stratified derivation cohort data based on the distance matrix from th... od Raiki Yoshimura (22676573)

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Comparison of performance of four machine learning models for early graft loss prediction. od Raiki Yoshimura (22676573)

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Comparison of performance of four machine learning models for long-term graft loss prediction. od Raiki Yoshimura (22676573)

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Predicting stratified groups in the validation cohort: (A) Workflows for the multi-class classification and hierarchical classification models are illustrated. od Raiki Yoshimura (22676573)

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Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups. od Raiki Yoshimura (22676573)

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Comparison of hierarchical classifier performance among four machine learning models for the early-loss, intermediate-loss, and late/no-loss groups. od Raiki Yoshimura (22676573)

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Prediction of early graft loss at the individual level and identification of crucial factors: (A) Survival curves for all patients who experienced graft loss are plotted. od Raiki Yoshimura (22676573)

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Comparison of multi-class classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups. od Raiki Yoshimura (22676573)

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Integrating Thermal Actuation and Acidochromism in Schiff Base Molecular Crystals od Sayak Nag (21387016)

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Integrating Thermal Actuation and Acidochromism in Schiff Base Molecular Crystals od Sayak Nag (21387016)

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

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

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

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

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

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

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