3 research outputs found
Quadratic Autoencoder (Q-AE) for Low-dose CT Denoising
Inspired by complexity and diversity of biological neurons, our group
proposed quadratic neurons by replacing the inner product in current artificial
neurons with a quadratic operation on input data, thereby enhancing the
capability of an individual neuron. Along this direction, we are motivated to
evaluate the power of quadratic neurons in popular network architectures,
simulating human-like learning in the form of quadratic-neuron-based deep
learning. Our prior theoretical studies have shown important merits of
quadratic neurons and networks in representation, efficiency, and
interpretability. In this paper, we use quadratic neurons to construct an
encoder-decoder structure, referred as the quadratic autoencoder, and apply it
to low-dose CT denoising. The experimental results on the Mayo low-dose CT
dataset demonstrate the utility of quadratic autoencoder in terms of image
denoising and model efficiency. To our best knowledge, this is the first time
that the deep learning approach is implemented with a new type of neurons and
demonstrates a significant potential in the medical imaging field
Soft-Autoencoder and Its Wavelet Shrinkage Interpretation
Recently, deep learning becomes the main focus of machine learning research
and has greatly impacted many fields. However, deep learning is criticized for
lack of interpretability. As a successful unsupervised model in deep learning,
the autoencoder embraces a wide spectrum of applications, yet it suffers from
the model opaqueness as well. In this paper, we propose a new type of
convolutional autoencoders, termed as Soft-Autoencoder (Soft-AE), in which the
activation functions of encoding layers are implemented with adaptable
soft-thresholding units while decoding layers are realized with linear units.
Consequently, Soft-AE can be naturally interpreted as a learned cascaded
wavelet shrinkage system. Our denoising experiments demonstrate that Soft-AE
not only is interpretable but also offers a competitive performance relative to
its counterparts. Furthermore, we propose a generalized linear unit (GeLU) and
its truncated variant (tGeLU) to allow autoencoder for more tasks from
denoising to deblurring
On Interpretability of Artificial Neural Networks: A Survey
Deep learning as represented by the artificial deep neural networks (DNNs)
has achieved great success in many important areas that deal with text, images,
videos, graphs, and so on. However, the black-box nature of DNNs has become one
of the primary obstacles for their wide acceptance in mission-critical
applications such as medical diagnosis and therapy. Due to the huge potential
of deep learning, interpreting neural networks has recently attracted much
research attention. In this paper, based on our comprehensive taxonomy, we
systematically review recent studies in understanding the mechanism of neural
networks, describe applications of interpretability especially in medicine, and
discuss future directions of interpretability research, such as in relation to
fuzzy logic and brain science