A. Riccardi
University of Bologna
20 Papers
231 Citations
A. Riccardi is an academic researcher from University of Bologna. The author has contributed to research in topics: Support vector machine & Gamma camera. The author has an hindex of 10, co-authored 20 publications.
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Papers
A novel featureless approach to mass detection in digital mammograms based on support vector machines
Renato Campanini,Danilo Nicola Dongiovanni,Emiro Iampieri,Nico Lanconelli,Matteo Masotti,Giuseppe Palermo,A. Riccardi,Matteo Roffilli +7 more
TL;DR: This work presents a novel approach to mass detection in digital mammograms that chooses not to extract any feature, for the detection of the region of interest; in contrast, it exploits all the information available on the image to codify the image with redundancy of information.
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Breast cancer metastases are molecularly distinct from their primary tumors.
Maurizio Vecchi,Stefano Confalonieri,Paolo Nuciforo,M A Viganò,Maria Capra,Marco Bianchi,D Nicosia,Fabrizio Bianchi,Viviana Galimberti,Giuseppe Viale,Giuseppe Palermo,A. Riccardi,Renato Campanini,Maria Grazia Daidone,M. A. Pierotti,Salvatore Pece,P P Di Fiore +16 more
TL;DR: The results show that breast cancer metastases are molecularly distinct from their primary tumors.
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An SVM classifier to separate false signals from microcalcifications in digital mammograms
Armando Bazzani,Alessandro Bevilacqua,Dante Bollini,Rosa Brancaccio,Renato Campanini,Nico Lanconelli,A. Riccardi,D. Romani +7 more
TL;DR: This paper compares the SVM classifier with an MLP (multi-layer perceptron) in the false-positive reduction phase of the detection scheme: a detected signal is considered either microcalcification or false signal, according to the value of a set of its features.
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Computer-aided detection of lung nodules via 3D fast radial transform, scale space representation, and Zernike MIP classification.
TL;DR: The system shows a novel approach to the problem of lung nodule detection in CT scans that relies on filtering techniques, image transforms, and descriptors rather than region growing and nodule segmentation and show little dependency on the different types of nodules, which is a good sign of robustness.
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•Proceedings Article
Automatic detection of clustered microcalcifications in digital mammograms using an SVM classifier.
Armando Bazzani,Alessandro Bevilacqua,Dante Bollini,Rosa Brancaccio,Renato Campanini,Nico Lanconelli,A. Riccardi,D. Romani,Gianluca Zamboni +8 more
- 01 Jan 2000
TL;DR: This paper investigates the performance of a Computer Aided Diagnosis (CAD) system for the detection of clustered microcalcifications in mammograms using a combination of two different methods, based on difference-image techniques and gaussianity statistical tests.
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