  In this paper, we propose an easily trained yet powerful representation
learning approach with performance highly competitive to deep neural networks
in a digital pathology image segmentation task. The method, called sparse
coding driven deep decision tree ensembles that we abbreviate as ScD2TE,
provides a new perspective on representation learning. We explore the
possibility of stacking several layers based on non-differentiable pairwise
modules and generate a densely concatenated architecture holding the
characteristics of feature map reuse and end-to-end dense learning. Under this
architecture, fast convolutional sparse coding is used to extract multi-level
features from the output of each layer. In this way, rich image appearance
models together with more contextual information are integrated by learning a
series of decision tree ensembles. The appearance and the high-level context
features of all the previous layers are seamlessly combined by concatenating
them to feed-forward as input, which in turn makes the outputs of subsequent
layers more accurate and the whole model efficient to train. Compared with deep
neural networks, our proposed ScD2TE does not require back-propagation
computation and depends on less hyper-parameters. ScD2TE is able to achieve a
fast end-to-end pixel-wise training in a layer-wise manner. We demonstrated the
superiority of our segmentation technique by evaluating it on the multi-disease
state and multi-organ dataset where consistently higher performances were
obtained for comparison against several state-of-the-art deep learning methods
such as convolutional neural networks (CNN), fully convolutional networks
(FCN), etc.
