
...

3. Choose the sform code name,  the default value is 'NIFTI_XFORM_SCANNER_ANAT'. Some scans without complete header files may loss this value(e.g. data from radiopaedia.org).In this case, please remember select sform name as 'OTHERS'. For more details about header files, please see this [site](https://brainder.org/2012/09/23/the-nifti-file-format/  "With a Title"). 

4. Click 'Run' button. After all the inference done, the progress bar window will be closed. 
   





#### Uncertainty:

In DABC-Net, we approximate Bayersian inference using [DropBlock](http://papers.nips.cc/paper/8271-dropblock-a-regularization-method-for-convolutional-networks), a form of Monte Carlo dropout. For more details about aleatory and epistemic uncertainty, please refer to this [paper](https://pdfs.semanticscholar.org/146f/8844a380191a3f883c3584df3d7a6a56a999.pdf).


	a/README.md		b/README.md
	...		...
107		107
108	3. Choose the sform code name, the default value is 'NIFTI_XFORM_SCANNER_ANAT'. Some scans without complete header files may loss this value(e.g. data from radiopaedia.org).In this case, please remember select sform name as 'OTHERS'. For more details about header files, please see this [site](https://brainder.org/2012/09/23/the-nifti-file-format/ "With a Title").	108	3. Choose the sform code name, the default value is 'NIFTI_XFORM_SCANNER_ANAT'. Some scans without complete header files may loss this value(e.g. data from radiopaedia.org).In this case, please remember select sform name as 'OTHERS'. For more details about header files, please see this [site](https://brainder.org/2012/09/23/the-nifti-file-format/ "With a Title").
109		109
110	4. Click 'Run' button. After all the inference done, the progress bar window will be closed.	110	4. Click 'Run' button. After all the inference done, the progress bar window will be closed.
111		111
112	![fig.2](fig/fig2.png)	112
113
114	Here are some examples:
115	![fig.4](fig/fig4.png )
116		113
117	#### Uncertainty:	114	#### Uncertainty:
118		115
119	In DABC-Net, we approximate Bayersian inference using [DropBlock](http://papers.nips.cc/paper/8271-dropblock-a-regularization-method-for-convolutional-networks), a form of Monte Carlo dropout. For more details about aleatory and epistemic uncertainty, please refer to this [paper](https://pdfs.semanticscholar.org/146f/8844a380191a3f883c3584df3d7a6a56a999.pdf).	116	In DABC-Net, we approximate Bayersian inference using [DropBlock](http://papers.nips.cc/paper/8271-dropblock-a-regularization-method-for-convolutional-networks), a form of Monte Carlo dropout. For more details about aleatory and epistemic uncertainty, please refer to this [paper](https://pdfs.semanticscholar.org/146f/8844a380191a3f883c3584df3d7a6a56a999.pdf).
120		117