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. 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

Fig. 5.

Fig. 5.

Fine-tuning TransVW reduces the annotation cost by 50%, 50%, 57%, 60%, and 80% in NCC, NCS, ECC, LCS, and BMS applications, respectively, when comparing with training from scratch. Moreover, TransVW reduces the annotation efforts by 17%, 24%, and 50% in NCC, LCS, and BMS applications, respectively, compared with state-of-the-art Models Genesis [5]. The horizontal gray and orange lines show the performance achieved by training from scratch and Models Genesis, respectively, when using the entire training data. The gray and orange bars indicate the minimum portion of training data that is required for training models from scratch and Models Genesis to achieve the comparable performance (based on the statistical analyses) with the corresponding models when training with the entire training data.