Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder

Accurate segmentation of anatomical structures in chest radiographs is essential for many computer-aided diagnosis tasks. In this paper we investigate the latest fully-convolutional architectures for the task of multi-class segmentation of the lungs field, heart and clavicles in a chest radiograph. In addition, we explore the influence of using different loss functions in the training process of a neural network for semantic segmentation. We evaluate all models on a common benchmark of 247 X-ray images from the JSRT database and ground-truth segmentation masks from the SCR dataset. Our best performing architecture, is a modified U-Net that benefits from pre-trained encoder weights. This model outperformed the current state-of-the-art methods tested on the same benchmark, with Jaccard overlap scores of 96.1% for lung fields, 90.6% for heart and 85.5% for clavicles.Comment: Presented at the First International Workshop on Thoracic Image Analysis (TIA), MICCAI 201

Using remote substituents to control solution structure and anion binding in lanthanide complexes.

A study of the anion-binding properties of three structurally related lanthanide complexes, which all contain chemically identical anion-binding motifs, has revealed dramatic differences in their anion affinity. These arise as a consequence of changes in the substitution pattern on the periphery of the molecule, at a substantial distance from the binding pocket. Herein, we explore these remote substituent effects and explain the observed behaviour through discussion of the way in which remote substituents can influence and control the global structure of a molecule through their demands upon conformational space. Peripheral modifications to a binuclear lanthanide motif derived from α,α′-bis(DO3 Ayl)-m-xylene are shown to result in dramatic changes to the binding constant for isophthalate. In this system, the parent compound displays considerable conformational flexibility, yet can be assumed to bind to isophthalate through a well-defined conformer. Addition of steric bulk remote from the binding site restricts conformational mobility, giving rise to an increase in binding constant on entropic grounds as long as the ideal binding conformation is not excluded from the available range of conformers

Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm

In recent years, a variety of relevance feedback (RF) schemes have been developed to improve the performance of content-based image retrieval (CBIR). Given user feedback information, the key to a RF scheme is how to select a subset of image features to construct a suitable dissimilarity measure. Among various RF schemes, biased discriminant analysis (BDA) based RF is one of the most promising. It is based on the observation that all positive samples are alike, while in general each negative sample is negative in its own way. However, to use BDA, the small sample size (SSS) problem is a big challenge, as users tend to give a small number of feedback samples. To explore solutions to this issue, this paper proposes a direct kernel BDA (DKBDA), which is less sensitive to SSS. An incremental DKBDA (IDKBDA) is also developed to speed up the analysis. Experimental results are reported on a real-world image collection to demonstrate that the proposed methods outperform the traditional kernel BDA (KBDA) and the support vector machine (SVM) based RF algorithms

Structural graph matching using the EM algorithm and singular value decomposition

This paper describes an efficient algorithm for inexact graph matching. The method is purely structural, that is, it uses only the edge or connectivity structure of the graph and does not draw on node or edge attributes. We make two contributions: 1) commencing from a probability distribution for matching errors, we show how the problem of graph matching can be posed as maximum-likelihood estimation using the apparatus of the EM algorithm; and 2) we cast the recovery of correspondence matches between the graph nodes in a matrix framework. This allows one to efficiently recover correspondence matches using the singular value decomposition. We experiment with the method on both real-world and synthetic data. Here, we demonstrate that the method offers comparable performance to more computationally demanding method

Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted Nuclear Norm

The nuclear norm is widely used as a convex surrogate of the rank function in compressive sensing for low rank matrix recovery with its applications in image recovery and signal processing. However, solving the nuclear norm based relaxed convex problem usually leads to a suboptimal solution of the original rank minimization problem. In this paper, we propose to perform a family of nonconvex surrogates of $L_0$-norm on the singular values of a matrix to approximate the rank function. This leads to a nonconvex nonsmooth minimization problem. Then we propose to solve the problem by Iteratively Reweighted Nuclear Norm (IRNN) algorithm. IRNN iteratively solves a Weighted Singular Value Thresholding (WSVT) problem, which has a closed form solution due to the special properties of the nonconvex surrogate functions. We also extend IRNN to solve the nonconvex problem with two or more blocks of variables. In theory, we prove that IRNN decreases the objective function value monotonically, and any limit point is a stationary point. Extensive experiments on both synthesized data and real images demonstrate that IRNN enhances the low-rank matrix recovery compared with state-of-the-art convex algorithms

Globalization, austerity and health equity politics : taming the inequality machine, and why it matters.

The recognition that globalization has an important role in explaining health inequalities has now moved into the mainstream. Much of that role relates to what has been called ‘[t]he inequality machine [that] is reshaping the planet’. At the same time, more attention must be paid to how the state can tame the inequality machine or compensate for its effects. I argue that governments have more flexibility in this respect than is often acknowledged. With an emphasis on current and recent social policy in Britain, I illustrate the need for researchers and practitioners to focus not only on external constraints associated with globalization but also on domestic political mechanisms and dynamics that may limit the extent to which governments can reduce health inequalities by addressing underlying social determinants