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@article{bb276804,
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@article{bb276807,
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@article{bb276818,
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@article{bb276819,
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        TITLE = "Automatic cervical cancer segmentation in multimodal magnetic
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@article{bb276823,
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@inproceedings{bb276845,
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@inproceedings{bb276846,
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@inproceedings{bb276847,
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@inproceedings{bb276848,
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@inproceedings{bb276849,
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@inproceedings{bb276850,
        AUTHOR = "Meng, Z. and Zhao, Z.C. and Su, F. and Guo, L.",
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Segmentation By Reconstructing Deep Segmentation Networks",
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        YEAR = "2021",
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@inproceedings{bb276851,
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@inproceedings{bb276852,
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        TITLE = "Cross-Domain Knowledge Transfer for Prediction of Chemosensitivity in
Ovarian Cancer Patients",
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@inproceedings{bb276853,
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Segmentation: A Radio-Frequency vs B-mode Comparison",
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@inproceedings{bb276854,
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Segment Qualifier",
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        YEAR = "2018",
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@inproceedings{bb276855,
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Normal and Pathological Cervical Cells in Pap Smear Images",
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@inproceedings{bb276856,
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        TITLE = "Deep Convolutional Neural Networks for Detecting Cellular Changes Due
to Malignancy",
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@inproceedings{bb276857,
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        TITLE = "Machine Learning Approach for Identification of miRNA-mRNA Regulatory
Modules in Ovarian Cancer",
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@inproceedings{bb276858,
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Digital Colposcopy",
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@inproceedings{bb276859,
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Screening",
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@inproceedings{bb276860,
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@inproceedings{bb276861,
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Segmentation in Pap Smear Images",
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@inproceedings{bb276862,
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@inproceedings{bb276863,
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