ArticleComputational and mathematical methods in medicine2021
A Computationally Virtual Histological Staining Method to Ovarian Cancer Tissue by Deep Generative Adversarial Networks.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 32 citations in OpenAlex.
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- Label-free whole slide virtual multi-staining using dual-excitation photon absorption remote sensing microscopy.Npj imaging · 2026Article
- A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology.Nature biomedical engineering · 2025Article
- Deep learning based semi-automated model can predict lineage in patients with pituitary neuroendocrine tumors.Acta neuropathologica communications · 2025Article
- Label-free optical microscopy with artificial intelligence: a new paradigm in pathology.Biophotonics discovery · 2025Review
- Deep learning-based virtual staining, segmentation, and classification in label-free photoacoustic histology of human specimens.Light, science & applications · 2024Article
- Virtual histological staining of unlabeled autopsy tissue.Nature communications · 2024Article
- Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy.Frontiers in immunology · 2024Article
- Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology.NPJ breast cancer · 2023Review
- Deep learning-enabled virtual histological staining of biological samples.Light, science & applications · 2023Review
- PETrans: De Novo Drug Design with Protein-Specific Encoding Based on Transfer Learning.International journal of molecular sciences · 2023Article
- Digital staining facilitates biomedical microscopy.Frontiers in bioinformatics · 2023Article
- Multi-TransDTI: Transformer for Drug-Target Interaction Prediction Based on Simple Universal Dictionaries with Multi-View Strategy.Biomolecules · 2022Article
- Deep Image Watermarking to JPEG Compression Based on Mixed-Frequency Channel Attention.Computational and mathematical methods in medicine · 2022Article
- An Enhanced Priori Knowledge GAN for CT Images Generation of Early Lung Nodules with Small-Size Labelled Samples.Oxidative medicine and cellular longevity · 2022Article
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Authors and funding
3 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Histological analysis to tissue samples is elemental for diagnosing the risk and severity of ovarian cancer. The commonly used Hematoxylin and Eosin (H&E) staining method involves complex steps and strict requirements, which would seriously impact the research of histological analysis of the ovarian cancer. Virtual histological staining by the Generative Adversarial Network (GAN) provides a feasible way for these problems, yet it is still a challenge of using deep learning technology since the amounts of data available are quite limited for training. Based on the idea of GAN, we propose a weakly supervised learning method to generate autofluorescence images of unstained ovarian tissue sections corresponding to H&E staining sections of ovarian tissue. Using the above method, we constructed the supervision conditions for the virtual staining process, which makes the image quality synthesized in the subsequent virtual staining stage more perfect. Through the doctors' evaluation of our results, the accuracy of ovarian cancer unstained fluorescence image generated by our method reached 93%. At the same time, we evaluated the image quality of the generated images, where the FID reached 175.969, the IS score reached 1.311, and the MS reached 0.717. Based on the image-to-image translation method, we use the data set constructed in the previous step to implement a virtual staining method that is accurate to tissue cells. The accuracy of staining through the doctor's assessment reached 97%. At the same time, the accuracy of visual evaluation based on deep learning reached 95%.
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