Counting Melanocytes with Trainable h-Maxima and Connected Component Layers
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-57793-2_32
Reference14 articles.
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2. Guan, S., Loew, M.: Understanding the ability of deep neural networks to count connected components in images. In: 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1–7. IEEE (2020)
3. He, S., Minn, K.T., Solnica-Krezel, L., Anastasio, M.A., Li, H.: Deeply-supervised density regression for automatic cell counting in microscopy images. Med. Image Anal. 68, 101892 (2021)
4. Lazard, T., et al.: Applying deep learning to melanocyte counting on fluorescent TRP1 labelled images of in vitro skin model. Image Anal. Stereol. (2022)
5. Najman, L., Talbot, H.: Mathematical Morphology: From Theory to Applications. Wiley, Hoboken (2013)
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