2015
DOI: 10.1016/j.artmed.2015.04.004
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An unsupervised feature learning framework for basal cell carcinoma image analysis

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Cited by 90 publications
(66 citation statements)
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“…Our work extends the method proposed in [30] in the sense that we also employ features that can best reconstruct the input image, but it differs from that work in two ways: 1) we use frequency domain features instead of spatial domain ones introduced in [30]. Specially, histopathological images are classified using Z-transform coefficients which will be shown to have better discriminative power with respect to the conventional Fourier transform coefficients and those used in [30].…”
Section: Related Workmentioning
confidence: 95%
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“…Our work extends the method proposed in [30] in the sense that we also employ features that can best reconstruct the input image, but it differs from that work in two ways: 1) we use frequency domain features instead of spatial domain ones introduced in [30]. Specially, histopathological images are classified using Z-transform coefficients which will be shown to have better discriminative power with respect to the conventional Fourier transform coefficients and those used in [30].…”
Section: Related Workmentioning
confidence: 95%
“…The problem of basal cell carcinoma detection is addressed in [26][27][28][29][30]. In [26], cells nuclei are segmented by maximally stable extreme regions (MSER) approach and then, color descriptors are extracted from the segmented regions at different scales.…”
Section: Related Workmentioning
confidence: 99%
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“…Many types of Deep Neural Networks exist, some of which are the 325 Deep Boltzmann Machines (Salakhutdinov & Hinton, 2009), the Restricted Deep Boltzmann machine (Hinton & Sejnowski, 1986), and the Convolutional Deep Belief Network (Lee et al, 2009). These methods have dramatically improved state-of-the-art natural language processing (Mikolov et al, 2013), computer vision (Ciresan et al, 2012), as well as many other applications such as drug 330 discovery and genomics (LeCun et al, 2015), and the analysis carcinoma images (Arevalo et al, 2015a). Convolutional neural networks have been applied for classifying mass lesions following mammography (Arevalo et al, 2015b) …”
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confidence: 99%