24 research outputs found
Image Quality Classification for DR Screening Using Convolutional Neural Networks
The quality of input images significantly affects the outcome of automated diabetic retinopathy screening systems. Current methods to identify image quality rely on hand-crafted geometric and structural features, that does not generalize well. We propose a new method for retinal image quality classification (IQC) that uses computational algorithms imitating the working of the human visual systems. The proposed method leverages on learned supervised information using convolutional neural networks (CNN), thus avoiding hand-engineered features. Our analysis shows that the learned features capture both geometric and structural information relevant for image quality classification. Experimental results conducted on a relatively large dataset demonstrates that the overall method can achieve high accuracy. We also show that effective features for IQC can be learned by full training of shallow CNN as well as by using transfer learning
Domain Generalization by Learning from Privileged Medical Imaging Information
Learning the ability to generalize knowledge between similar contexts is
particularly important in medical imaging as data distributions can shift
substantially from one hospital to another, or even from one machine to
another. To strengthen generalization, most state-of-the-art techniques inject
knowledge of the data distribution shifts by enforcing constraints on learned
features or regularizing parameters. We offer an alternative approach: Learning
from Privileged Medical Imaging Information (LPMII). We show that using some
privileged information such as tumor shape or location leads to stronger domain
generalization ability than current state-of-the-art techniques. This paper
demonstrates that by using privileged information to predict the severity of
intra-layer retinal fluid in optical coherence tomography scans, the
classification accuracy of a deep learning model operating on
out-of-distribution data improves from to . This paper provides
a strong starting point for using privileged information in other medical
problems requiring generalization
Volumetric image analysis: optical flow, registration and segmentation
This thesis presents more accurate and efficient methods for volumetric image analysis in terms of Optical Flow, Registration and Segmentation. Firstly, a relationship between the estimation accuracy and the required amount of smoothness for motion estimation from a robust statistics perspective is developed. Next, a fast and accurate non-rigid registration method for intra-modality volumetric images that exploits the information provided by an order statistics-based segmentation method, to find the important regions for registration is presented. Finally, two new methods that improve the accuracy and efficiency of the identification of underlying structures in data that is contaminated with noise and outliers are proposed
Decision sciences
Complexities of dynamic volumetric imaging challenge the available computer vision techniques on a number of different fronts. This paper examines the relationship between the estimation accuracy and required amount of smoothness for a general solution from a robust statistics perspective. We show that a (surprisingly) small amount of local smoothing is required to satisfy both the necessary and sufficient conditions for accurate optic flow estimation. This notion is called 'just enough' smoothing, and its proper implementation has a profound effect on the preservation of local information in processing 3D dynamic scans. To demonstrate the effect of 'just enough' smoothing, a robust 3D optic flow method with quantized local smoothing is presented, and the effect of local smoothing on the accuracy of motion estimation in dynamic lung CT images is examined using both synthetic and real image sequences with ground truth