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An image-retrieval aided diagnosis system for clustered microcalcifications.

, , , и . ISBI, стр. 1076-1079. IEEE, (2016)

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Etsushi Nishikawa

Isokichi Nishikawa

Robert M Zoske

Robert M Gillenkirch

Robert M Stutz

 

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