ArticleProceedings. IEEE International Symposium on Biomedical Imaging2022
SHARP-GAN: SHARPNESS LOSS REGULARIZED GAN FOR HISTOPATHOLOGY IMAGE SYNTHESIS.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Topology-Guided Multi-Class Cell Context Generation for Digital Pathology.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2023Article
- Panoptic quality should be avoided as a metric for assessing cell nuclei segmentation and classification in digital pathology.Scientific reports · 2023Article
- SIAN: STYLE-GUIDED INSTANCE-ADAPTIVE NORMALIZATION FOR MULTI-ORGAN HISTOPATHOLOGY IMAGE SYNTHESIS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- ENHANCED SHARP-GAN FOR HISTOPATHOLOGY IMAGE SYNTHESIS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- Functional Output Regression for Machine Learning in Materials Science.Journal of chemical information and modeling · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Existing deep learning-based approaches for histopathology image analysis require large annotated training sets to achieve good performance; but annotating histopathology images is slow and resource-intensive. Conditional generative adversarial networks have been applied to generate synthetic histopathology images to alleviate this issue, but current approaches fail to generate clear contours for overlapped and touching nuclei. In this study, We propose a sharpness loss regularized generative adversarial network to synthesize realistic histopathology images. The proposed network uses normalized nucleus distance map rather than the binary mask to encode nuclei contour information. The proposed sharpness loss enhances the contrast of nuclei contour pixels. The proposed method is evaluated using four image quality metrics and segmentation results on two public datasets. Both quantitative and qualitative results demonstrate that the proposed approach can generate realistic histopathology images with clear nuclei contours.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.