. Author manuscript; available in PMC: 2022 Oct 28.

Published in final edited form as: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2022 Sep 27;2022:20792–20802. doi: 10.1109/cvpr52688.2022.02016

Table 3. Comparison with restorative self-supervised method:

We apply our DiRA to the TransVW as the SOTA restorative self-supervised method. DiRA enhances TransVW by conserving more fine-grained details, resulting in performance boosts in four 3D downstream tasks.

Dataset	Method
Dataset	Random	TransVW [26]	DiRA_TransVW
LUNA	94.25±5.07	98.46±0.30	98.87±0.61 (↑ 0.41)
LIDC-IDRI	74.05±1.97	77.33±0.52	77.51±1.36 (↑ 0.18)
LiTS	79.76±5.42	86.53±1.30	86.85±0.81 (↑ 0.32)
BraTS	59.87±4.04	68.82±0.38	69.57±1.13 (↑ 0.75)
PE-CAD	80.36±3.58	87.07±2.83	86.91±3.27