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Image Search Results
Journal: Cancers
Article Title: A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images
doi: 10.3390/cancers14051159
Figure Lengend Snippet: Representative manually drawing annotation images for neoplastic labels on liquid-based cytology (LBC) slides. The LBC case ( A ) was diagnosed as HSIL (high-grade squamous intraepithelial lesion) based on the representative neoplastic squamous epithelial cells with increase in nuclear/cytoplasmic ratio and nuclear atypia ( B – D ). The LBC case ( E ) was diagnosed as SCC (squamous cell carcinoma) based on the representative neoplastic squamous epithelial cells with HSIL features ( F – H ). Representative neoplastic cells were roughly annotated using in-house on-line drawing tools.
Article Snippet: A total of 3121
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Journal: Cancers
Article Title: A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images
doi: 10.3390/cancers14051159
Figure Lengend Snippet: A representative example of neoplastic true positive prediction outputs on a liquid-based cytology (LBC) case from test sets. In the neoplastic whole-slide image (WSI) of LBC specimen ( A ), the heatmap image ( B ) shows a true positive prediction of neoplastic epithelial cells in high probability tiles ( C , D ), which correspond, respectively, to neoplastic epithelial cells ( E – G ) equivalent to HSIL (high-grade squamous intraepithelial lesion). On the other hand, in low probability tiles ( H , I ) of the same heatmap image ( B ), there are no evidence of neoplastic cells.
Article Snippet: A total of 3121
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Journal: Cancers
Article Title: A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images
doi: 10.3390/cancers14051159
Figure Lengend Snippet: A representative example of neoplastic true negative prediction outputs on a liquid-based cytology (LBC) case from test sets. In the NILM (negative for intraepithelial lesion or malignancy) whole slide image (WSI) of LBC specimen ( A ), the heatmap image ( B ) shows true negative prediction of neoplastic epithelial cells which correspond, respectively, to non-neoplastic epithelial cells ( C ). Moreover, in very low probability tiles ( D , E ) of the same heatmap image ( B ), there are no evidence of neoplastic cells.
Article Snippet: A total of 3121
Techniques:
Journal: Cancers
Article Title: A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images
doi: 10.3390/cancers14051159
Figure Lengend Snippet: A representative example of neoplastic false positive prediction outputs on a liquid-based cytology (LBC) case from test sets. Cytopathologically, ( A ) is a NILM (negative for intraepithelial lesion or malignancy) whole-slide image (WSI) of LBC specimen. The heatmap image ( B ) exhibited false positive predictions of neoplastic tiles ( C , E ). In ( C ), there are parabasal cells with a slightly high nuclear cytoplasmic (N/C) ratio with dense chromatin appearance due to the cellular overlapping ( D ). In ( E ), there are cell clusters of squamous epithelial cells and cervical gland cells with slightly high N/C ratios and a dense chromatin appearance due to the cellular overlapping.
Article Snippet: A total of 3121
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