Automatic Classification for Pathological Prostate Images Based on Fractal Analysis

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Deep learning of feature representation with multiple instance learning for medical image analysis (2014, background): “In medical image analysis, it is common to design a group of specific features [1, 2] for a high-level task such as classification and segmentation [3].”
Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features (2017, background): “Medical image segmentation methods can be generally classified into three categories: supervised learning [29], weakly supervised [48] and unsupervised [32].”
Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles (2016, background): “…Farjam et al., 2007; Fatima et al., 2014; Glotsos et al., 2008; Gunduz et al., 2004; Gupta et al., 2001; Hall et al., 2008; Huang and Lai, 2010; Huang and Lee, 2009; Jafari-Khouzani and Soltanian-Zadeh, 2003; Kong et al., 2007; Land et al., 2008; Lessmann et al., 2007; Meng et al., 2010;…”
Weakly supervised histopathology cancer image segmentation and classification (2014, methodology): “As far as we know, the code and images used in Huang and Lee (2009), Tabesh et al. (2007), and Esgiar et al. (2002) are not publicly accessible.”