. Author manuscript; available in PMC: 2022 Oct 1.

Published in final edited form as: IEEE Trans Med Imaging. 2021 Sep 30;40(10):2857–2868. doi: 10.1109/TMI.2021.3060634

Table II.

TransVW significantly outperforms training from scratch, and achieves the best or comparable performance in five 3D target applications over five self-supervised and three publicly available supervised pre-trained 3D models. We evaluate the classification (i.e., NCC and ECC) and segmentation (i.e., NCS, LCS, and BMS) target tasks under auc and iou metrics, respectively. For each target task, we show the average performance and standard deviation across ten runs, and further perform independent two sample t-test between the best (bolded) vs. others and highlighted boxes in green when they are not statistically significantly different at p = 0.05 level.

Pre-training task			Target tasks
Method	Supervised	Dataset	NCC (%)	NCS¹ (%)	ECC² (%)	LCS³ (%)	BMS⁴(%)
Random	✕	N/A	94.25±5.07	74.05±1.97	80.36±3.58	79.76±5.42	59.87±4.04
NiftyNet [16]	✓	Pancreas-CT, BTCV [23]	94.14±4.57	52.98±2.05	77.33±8.05	83.23±1.05	60.78±1.60
MedicalNet [17]	✓	3DSeg-8 [17]	95.80±0.51	75.68±0.32	86.43±1.44	85.52±0.58	66.09±1.35
I3D [15]	✓	Kinetics [15]	98.26±0.27	71.58±0.55	80.55±1.11	70.65±4.26	67.83±0.75
Inpainting [3]	✕	LUNA16 [7]	95.12±1.74	76.02±0.55	84.08±2.34	81.36±4.83	61.38±3.84
Context restoration [4]	✕	LUNA16 [7]	94.90±1.18	75.55±0.82	82.15±3.3	82.82±2.35	59.05±2.83
Rotation [2]	✕	LUNA16 [7]	94.42±1.78	76.13±.61	83.40±2.71	83.15±1.41	60.53±5.22
Rubik’s Cube [14]	✕	LUNA16 [7]	96.24±1.27	72.87±0.16	80.49±4.64	75.59±0.20	62.75±1.93
Models Genesis [5]	✕	LUNA16 [7]	98.07±0.59	77.41±0.40	87.2±2.87	85.1±2.15	67.96±1.29
VW classification	✕	LUNA16 [7]	97.49±0.45	76.93±0.87	84.25±3.91	84.14±1.78	64.02±0.98
VW restoration	✕	LUNA16 [7]	98.10±0.19	77.70±0.59	86.20±3.21	84.57±2.20	67.78±0.57
TransVW	✕	LUNA16 [7]	98.46±0.30	77.33±0.52	87.07±2.83	86.53±1.30	68.82±0.38

[24] holds a Dice of 74.05% vs. 75.85 ± 0.83% (ours)

[25] holds an AUC of 87.06% vs. 87.07%±2.83% (ours)

[26] holds a Dice of 95.76% vs. 95.84% ± 0.07% (ours using nnU-Net framework)

⁴

MR Flair images are only utilized for segmenting brain tumors, so the results are not submitted to BraTS-2018.