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[25]. The original tool did not include ecological momentary assessment methods (EMA), and preceded the more widespread use of technology-assisted methods such as automated recalls (see Supplementary File Table S2 for the tool). The tool includes an assessment of the appropriateness of the method...
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medical image segmentation datasets, which were obtained from various sources across the internet, including the Cancer Imaging Archive (TCIA)34, Kaggle, Grand-Challenge, Scientific Data, CodaLab, and segmentation challenges in the Medical Image Computing and Computer Assisted Intervention Society (MICCAI...
Deep neural networks have been integrated into the whole clinical decision procedure which can improve the efficiency of diagnosis and alleviate the heavy workload of physicians. Since most neural networks are supervised, their performance heavily depend
In: Medical image computing and computer-assisted intervention, Pt Iii, 2015. vol. 9351, pp. 234-241. Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J. UNet++: a nested U-Net architecture for medical image segmentation. In: 4th deep learning in medical image analysis (DLMIA) Workshop. ...
Czempiel, T. et al. TeCNO: surgical phase recognition with multi-stage temporal convolutional networks. InMedical Image Computing and Computer Assisted Intervention—MICCAI 2020. Lecture Notes in Computer Science, vol 12263 (eds. Martel, A. L. et al.) 343–352 (Springer, 2020). ...