Fetching the paper…
Reading the bibliography…
The detection of nuclei is one of the most fundamental components of computational pathology.
Sirinukunwattana, K., et al.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. Trans. Medical Imaging (2016)
2016
Earlier work this paper cites.
Brieu, N., Schmidt, G.: Learning size adaptive local maxima selection for robust nuclei detection in histopathology images. In: ISBI (2017)
2017
Earlier work this paper cites.
Carstens, J., et al.: Spatial computation of intratumoral t cells correlates with survival of patients with pancreatic cancer. Nature comm. (2017)
2017
Earlier work this paper cites.
Chartsias, A., et al.: Adversarial image synthesis for unpaired multi-modal cardiac data. In: Intl Wksp on Simulation and Synthesis in Medical Imaging (2017)
2017
Earlier work this paper cites.
Ciompi, F., et al.: The importance of stain normalization in colorectal tissue classification with convolutional networks. In: ISBI (2017)
2017
Earlier work this paper cites.
Kumar, N., et al.: A dataset and a technique for generalized nuclear segmentation for computational pathology. Transactions on Medical Imaging (2017)
2017
Earlier work this paper cites.
Vandenberghe, M.E., Barker, C., et al.: Relevance of deep learning to facilitate the diagnosis of her2 status in breast cancer. Scientific reports (2017)
2017
Cited alongside, same era.
Zhu, J.Y., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint (2017)
2017
Cited alongside, same era.
Höfener, H., et al.: Deep learning nuclei detection: A simple approach can deliver state-of-the-art results. Comput. Medical Imaging and Graphics (2018)
2018
Cited alongside, same era.
Kapil, A., et al.: Deep semi supervised generative learning for automated tumor proportion scoring on nsclc tissue needle biopsies. Scientific reports (2018)
2018
Cited alongside, same era.
Naylor, P., et al.: Segmentation of nuclei in histopathology images by deep regression of the distance map. Trans. on Medical Imaging (2018)
2018
Zanjani, F., van der Laak, J., et al.: Histopathology stain-color normalization using deep generative models. In: MIDL (2018)
2018
Later among the works it cites.
Hou, L., et al.: Robust histopathology image analysis: To label or to synthesize? In: CVPR (2019)
2019
Closest in time.
2019
Closest in time.
Qu, H., et al.: Weakly supervised deep nuclei segmentation using points annotation in histopathology images. In: MIDL (2019)
2019
Closest in time.
Rivenson, Y., et al.: Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nature Biomedical Eng. (2019)
2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.