UniToChest / Git / Diff of /README.md

Datasets:

GeorgeSullivan/

UniToChest

Downloads: 1

Data:

Image Specialty:

Oncology

Radiology Medical Imaging Technique:

CT Medical Imaging Region:

Chest

Lungs Clinical Purpose:

Diagnosis Task:

Detection

Segmentation License:

Unknown

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<p><strong>References</strong></p>
<p>[1] Daniele Perlo, «UniToChest». Zenodo, dic. 22, 2021. doi: 10.5281/zenodo.5797912.<br>
[2] H. A. H. Chaudhry et&nbsp;al., «UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans», en Image Analysis and Processing – ICIAP 2022, S. Sclaroff, C. Distante, M. Leo, G. M. Farinella, y F. Tombari, Eds., Cham: Springer International Publishing, 2022, pp. 185-196. doi: 10.1007/978-3-031-06427-2_16.<br>
[3] J. Hofmanninger, F. Prayer, J. Pan, et al., "Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem," Eur Radiol Exp, vol. 4, p. 50, 2020. doi: 10.1186/s41747-020-00173-2.</p></div></div></div>

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20	</ul>	20	</ul>
21	<p><strong>References</strong></p>	21	<p><strong>References</strong></p>
22	<p>[1] Daniele Perlo, «UniToChest». Zenodo, dic. 22, 2021. doi: 10.5281/zenodo.5797912.<br>	22	<p>[1] Daniele Perlo, «UniToChest». Zenodo, dic. 22, 2021. doi: 10.5281/zenodo.5797912.<br>
23	[2] H. A. H. Chaudhry et al., «UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans», en Image Analysis and Processing – ICIAP 2022, S. Sclaroff, C. Distante, M. Leo, G. M. Farinella, y F. Tombari, Eds., Cham: Springer International Publishing, 2022, pp. 185-196. doi: 10.1007/978-3-031-06427-2_16.<br>	23	[2] H. A. H. Chaudhry et al., «UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans», en Image Analysis and Processing – ICIAP 2022, S. Sclaroff, C. Distante, M. Leo, G. M. Farinella, y F. Tombari, Eds., Cham: Springer International Publishing, 2022, pp. 185-196. doi: 10.1007/978-3-031-06427-2_16.<br>
24	[3] J. Hofmanninger, F. Prayer, J. Pan, et al., "Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem," Eur Radiol Exp, vol. 4, p. 50, 2020. doi: 10.1186/s41747-020-00173-2.</p></div></div></div><button aria-label="View more about this dataset" tabindex="0" role="button" aria-expanded="false" class="sc-dHSNJi LIdZl" style="margin: 24px 0px 8px;"><span class="google-symbols notranslate MuiIcon-root MuiIcon-fontSizeMedium sc-kmqAS HYlEG notranslate css-1jgtvd5" aria-hidden="true">expand_more</span><span class="sc-ifkGpL jcrTve">View more</span></button></div></div>	24	[3] J. Hofmanninger, F. Prayer, J. Pan, et al., "Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem," Eur Radiol Exp, vol. 4, p. 50, 2020. doi: 10.1186/s41747-020-00173-2.</p></div></div></div>