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Learning better deep features for the prediction of occult invasive disease in ductal carcinoma in situ through transfer learning.

, , , , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10575 of SPIE Proceedings, page 105752R. SPIE, (2018)

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DCIS AI-TIL: Ductal Carcinoma In Situ Tumour Infiltrating Lymphocyte Scoring Using Artificial Intelligence., , , , , , , , , and 6 other author(s). AIIIMA/MIABID@MICCAI, volume 13602 of Lecture Notes in Computer Science, page 164-175. Springer, (2022)Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features?, , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10134 of SPIE Proceedings, page 101342X. SPIE, (2017)Automated Dcis Identification From Multiplex Immunohistochemistry Using Generative Adversarial Networks., , , , , , , , and . ISBI, page 1-5. IEEE, (2022)Learning better deep features for the prediction of occult invasive disease in ductal carcinoma in situ through transfer learning., , , , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10575 of SPIE Proceedings, page 105752R. SPIE, (2018)Improving classification with forced labeling of other related classes: application to prediction of upstaged ductal carcinoma in situ using mammographic features., , , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10575 of SPIE Proceedings, page 105750R. SPIE, (2018)A new method to accurately identify single nucleotide variants using small FFPE breast samples., , , , , , , , , and . Briefings Bioinform., (2021)Anomaly Detection of Calcifications in Mammography Based on 11, 000 Negative Cases., , , , , , , , , and . IEEE Trans. Biomed. Eng., 69 (5): 1639-1650 (2022)Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features., , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10134 of SPIE Proceedings, page 101341I. SPIE, (2017)A multitask deep learning method in simultaneously predicting occult invasive disease in ductal carcinoma in-situ and segmenting microcalcifications in mammography., , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 11314 of SPIE Proceedings, SPIE, (2020)Prediction of Upstaged Ductal Carcinoma In Situ Using Forced Labeling and Domain Adaptation., , , , , , , and . IEEE Trans. Biomed. Eng., 67 (6): 1565-1572 (2020)