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Image Search Results
Journal: Reviews in Obstetrics and Gynecology
Article Title: The Impact of Liquid-Based Cytology in Decreasing the Incidence of Cervical Cancer
doi:
Figure Lengend Snippet: ThinPrep® System (Hologic, Inc, Marlborough, MA) sample processing and analysis. Processing a Thin-Prep Pap test sample for analysis involves three essential steps: (i) Rotation of the filter within the sample vial creates currents in the fluid that separate debris and disperse mucus yet preserve cell morphology; (ii) A gentle vacuum (controlled by software for the ThinPrep Processor) created within the filter collects cells on the exterior surface of the membrane; (iii) After the cells are collected on the membrane, the filter is inverted and gently pressed against the microscope slide. Natural attraction and slight positive air pressure cause the cells to adhere to the slide, resulting in an even distribution of cells in a defined circular area.
Article Snippet: Klinkhamer PJ et al 36 , 10 (1995–2000) ,
Techniques: Gentle, Software, Membrane, Microscopy
Journal: Reviews in Obstetrics and Gynecology
Article Title: The Impact of Liquid-Based Cytology in Decreasing the Incidence of Cervical Cancer
doi:
Figure Lengend Snippet: Table 2 Meta-Analyses Comparing LBC and CP for Detection of Cervical Abnormalities
Article Snippet: Klinkhamer PJ et al 36 , 10 (1995–2000) ,
Techniques: Biomarker Discovery
Journal: Reviews in Obstetrics and Gynecology
Article Title: The Impact of Liquid-Based Cytology in Decreasing the Incidence of Cervical Cancer
doi:
Figure Lengend Snippet: The CAP Laboratory Survey: Median Reporting Rates (50th Percentile) for Conventional Papanicolaou (Pap), SurePath Pap, and ThinPrep Pap Cytologic Preparations Across the Major Clinical Cytologic Categories 37
Article Snippet: Klinkhamer PJ et al 36 , 10 (1995–2000) ,
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: 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
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 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
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 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
Techniques: