Computer-aided detection and classification of microcalcifications in mammograms: a survey

By Heng-Da Cheng, Xiaopeng Cai, Xiaowei Chen, Liming Hu, Xueling Lou. Pattern Recognition, 2003

596 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a88d12a88e711702041bedeb5dfe482af96cc959

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Citations in the corpus

A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs (2007, unknown): “[55] lists almost 200 references on computer-aided detection and classification of microcalcifications, including methods for the visual enhancement of microcalcifications, segmentation, detection, analysis of malignancy, and strategies for the evaluation of detection algorithms.”
Automated breast cancer detection and classification using ultrasound images: A survey (2015, background): “Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost.”
A review of automatic mass detection and segmentation in mammographic images (2010, background): “Cheng et al. (2003) covers the detection and classiﬁcation of micro-calci-ﬁcations.”
Medical image analysis with artificial neural networks (2010, methodology): “22 other approaches rather than ANN in detection and classification of microcalcifications and masses in mammograms, details can be found in [61] and [64].”
Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances (2009, background): “Of all the known cancers, breast cancer is a major concern among women.”
Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach (2014, background): “It is generally accepted that mass detection is a more challenging problem than the detection of micro-calcifications, not only for the large variation in size and shape in which masses can appear in a mammogram but also because masses often exhibit poor image contrast [4].”
Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation (2015, methodology): “At present, examples of CADe used in clinical practice include polyp detection for colon cancer screening [9], [10], lung nodule detection for lung cancer screening [11], [12] or breast cancer screening with mammography [13].”
Machine Learning and Radiology (2012, methodology): “In many radiology applications, e.g. mammography and colon CAD, computerized CADx systems have shown comparable, or even higher, performance compared with well-trained and experienced radiologists and technologists (Cheng et al., 2003; Doi, 2005, 2007; Giger et al., 2001).”
Approaches for automated detection and classification of masses in mammograms (2006, unknown): “Microcalcifications and masses are two important early signs of the diseases [198].”
Artificial intelligence in Cancer Imaging: Clinical Challenges and Applications (2019, background): “…metastases in MRIs to improve radiology interpretation time while maintaining high detection sensitivity, 9 locate microcalcification clusters in screening mammography as an indicator of early breast carcinoma, 10 and more generally has improved radiologist sensitivity for detecting abnormalities.”