Two experiments are included in this study using the Network in Network structure (NIN) and the Fully Convoluted Network (FCN), respectively. The NIN is used as is from Lin et al. 2013. Whereas, the FCN is adapted from Long et al. 2015 and is extended from a LeNet as seen in LeCun et al. 1998. The convolutionalization step transforms fully connected layers into convolution layers, which enables a classification net to output a heatmap.
patchk.pycreates image patches and clusters them using the K-means methodimage-patches/folder that encompasses the resulting patchesbuildDatasetcreates a HDF5 dataset provided the list of patches- Files with the prefix
bighandle the model's training - Files with the suffix
surgerycomprehend the 2-dimensional convolutions and convolutionalization.
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