14 research outputs found
A Divide-and-Conquer Approach Towards Understanding Deep Networks
Deep neural networks have achieved tremendous success in various fields including medical image segmentation. However, they have long been criticized for being a black-box, in that interpretation, understanding and correcting architectures is difficult as there is no general theory for deep neural network design. Previously, precision learning was proposed to fuse deep architectures and traditional approaches. Deep networks constructed in this way benefit from the original known operator, have fewer parameters, and improved interpretability. However, they do not yield state-of-the-art performance in all applications. In this paper, we propose to analyze deep networks using known operators, by adopting a divide-and-conquer strategy to replace network components, whilst retaining networks performance. The task of retinal vessel segmentation is investigated for this purpose. We start with a high-performance U-Net and show by step-by-step conversion that we are able to divide the network into modules of known operators. The results indicate that a combination of a trainable guided filter and a trainable version of the Frangi filter yields a performance at the level of U-Net (AUC 0.974 vs. 0.972) with a tremendous reduction in parameters (111, 536 vs. 9, 575). In addition, the trained layers can be mapped back into their original algorithmic interpretation and analyzed using standard tools of signal processing
Precision Learning: Towards Use of Known Operators in Neural Networks
In this paper, we consider the use of prior knowledge within neural networks.
In particular, we investigate the effect of a known transform within the
mapping from input data space to the output domain. We demonstrate that use of
known transforms is able to change maximal error bounds.
In order to explore the effect further, we consider the problem of X-ray
material decomposition as an example to incorporate additional prior knowledge.
We demonstrate that inclusion of a non-linear function known from the physical
properties of the system is able to reduce prediction errors therewith
improving prediction quality from SSIM values of 0.54 to 0.88.
This approach is applicable to a wide set of applications in physics and
signal processing that provide prior knowledge on such transforms. Also maximal
error estimation and network understanding could be facilitated within the
context of precision learning.Comment: accepted on ICPR 201
Imitation Learning Network for Fundus Image Registration Using a Divide-And-Conquer Approach
Comparison of microvascular circulation on fundoscopic images is a non-invasive clinical indication for the diagnosis and monitoring of diseases, such as diabetes and hypertensions. The differences between intra-patient images can be assessed quantitatively by registering serial acquisitions. Due to the variability of the images (i.e. contrast, luminosity) and the anatomical changes of the retina, the registration of fundus images remains a challenging task. Recently, several deep learning approaches have been proposed to register fundus images in an end-to-end fashion, achieving remarkable results. However, the results are diffcult to interpret and analyze. In this work, we propose an imitation learning framework for the registration of 2D color funduscopic images for a wide range of applications such as disease monitoring, image stitching and super-resolution. We follow a divide-and-conquer approach to improve the interpretability of the proposed network, and analyze both the influence of the input image and the hyperparameters on the registration result. The results show that the proposed registration network reduces the initial target registration error up to 95
User Loss -- A Forced-Choice-Inspired Approach to Train Neural Networks directly by User Interaction
In this paper, we investigate whether is it possible to train a neural
network directly from user inputs. We consider this approach to be highly
relevant for applications in which the point of optimality is not well-defined
and user-dependent. Our application is medical image denoising which is
essential in fluoroscopy imaging. In this field every user, i.e. physician, has
a different flavor and image quality needs to be tailored towards each
individual.
To address this important problem, we propose to construct a loss function
derived from a forced-choice experiment. In order to make the learning problem
feasible, we operate in the domain of precision learning, i.e., we inspire the
network architecture by traditional signal processing methods in order to
reduce the number of trainable parameters. The algorithm that was used for this
is a Laplacian pyramid with only six trainable parameters.
In the experimental results, we demonstrate that two image experts who prefer
different filter characteristics between sharpness and de-noising can be
created using our approach. Also models trained for a specific user perform
best on this users test data. This approach opens the way towards
implementation of direct user feedback in deep learning and is applicable for a
wide range of application.Comment: Accepted on BVM 2019; Extended ArXiv Version with additional figures
and detail