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Optimizing wavelet transform based on supervised learning for detection of microcalcifications in digital mammograms.

, , , , , and . ICIP (3), page 152-155. IEEE Computer Society, (1995)

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Optimization and FROC Analysis of Rule-Based Detection Schemes Using a Multiobjective Approach., , and . IEEE Trans. Med. Imaging, 17 (6): 1089-1093 (1998)A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications., , , and . IEEE Trans. Med. Imaging, 24 (3): 371-380 (2005)A similarity learning approach to content-based image retrieval: application to digital mammography., , , , and . IEEE Trans. Med. Imaging, 23 (10): 1233-1244 (2004)Estimating the Accuracy Level Among Individual Detections in Clustered Microcalcifications., , and . IEEE Trans. Med. Imaging, 36 (5): 1162-1171 (2017)Intelligent CAD workstation for breast imaging using similarity to known lesions and multiple visual prompt aids., , , , , , , and . Medical Imaging: Image Processing, volume 4684 of SPIE Proceedings, SPIE, (2002)Benefits of Computer-Aided Diagnosis (CAD) in Mammographic Diagnosis of Malignant and Benign Clustered Microcalcifications., , , , , and . Digital Mammography / IWDM, volume 13 of Computational Imaging and Vision, page 215-220. Springer, (1998)Modeling the Effect of Computer-Aided Detection on the Sensitivity of Screening Mammography.. Digital Mammography / IWDM, volume 4046 of Lecture Notes in Computer Science, page 46-53. Springer, (2006)Changes in frequency of recall recommendations of examinations depicting cancer with the availability of either priors or digital breast tomosynthesis., , , , , , , , , and 1 other author(s). Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 9787 of SPIE Proceedings, page 97871A. SPIE, (2016)Oculomotor behaviour of radiologists reading digital breast tomosynthesis (DBT)., , , , and . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 10952 of SPIE Proceedings, page 1095204. SPIE, (2019)Cross-Organ, Cross-Modality Transfer Learning: Feasibility Study for Segmentation and Classification., and . IEEE Access, (2020)