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Reducing the effect of false positives in classification of detected clustered microcalcifications.

, , and . ISBI, page 253-256. IEEE, (2018)

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Estimating the Accuracy Level Among Individual Detections in Clustered Microcalcifications., , and . IEEE Trans. Med. Imaging, 36 (5): 1162-1171 (2017)Multi-task Learning for Detection and Classification of Cancer in Screening Mammography., , , , and . MICCAI (6), volume 12266 of Lecture Notes in Computer Science, page 241-250. Springer, (2020)Reducing the effect of false positives in classification of detected clustered microcalcifications., , and . ISBI, page 253-256. IEEE, (2018)Improving uniformity in detection performance of clustered microcalcifications in mammograms., and . ICIP, page 842-846. IEEE, (2015)Case-based decision strategy using outlier probability in detection of microcalcifications in mammographic lesions., and . ICIP, page 3409-3413. IEEE, (2016)Single stain normalization for IHC whole slide images., and . Medical Imaging: Digital Pathology, volume 10581 of SPIE Proceedings, page 1058104. SPIE, (2018)Reading Mammography with Multiple Prior Exams., , and . ISBI, page 1116-1119. IEEE, (2020)Pneumoperitoneum Detection In Chest X-Ray By A Deep Learning Ensemble With Model Explainability., , and . ISBI, page 1637-1641. IEEE, (2021)Point process modeling for determining detection accuracy of mammographic microcalcifications., and . ICIP, page 1357-1361. IEEE, (2017)Case-adaptive decision rule for detection of clustered microcalcifications in mammograms., and . ISBI, page 1147-1150. IEEE, (2015)