Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation

Federated learning (FL) enables multiple client medical institutes collaboratively train a deep learning (DL) model with privacy protection. However, the performance of FL can be constrained by the limited availability of labeled data in small institutes and the heterogeneous (i.e., non-i.i.d.) data distribution across institutes. Though data augmentation has been a proven technique to boost the generalization capabilities of conventional centralized DL as a "free lunch", its application in FL is largely underexplored. Notably, constrained by costly labeling, 3D medical segmentation generally relies on data augmentation. In this work, we aim to develop a vicinal feature-level data augmentation (VFDA) scheme to efficiently alleviate the local feature shift and facilitate collaborative training for privacy-aware FL segmentation. We take both the inner- and inter-institute divergence into consideration, without the need for cross-institute transfer of raw data or their mixup. Specifically, we exploit the batch-wise feature statistics (e.g., mean and standard deviation) in each institute to abstractly represent the discrepancy of data, and model each feature statistic probabilistically via a Gaussian prototype, with the mean corresponding to the original statistic and the variance quantifying the augmentation scope. From the vicinal risk minimization perspective, novel feature statistics can be drawn from the Gaussian distribution to fulfill augmentation. The variance is explicitly derived by the data bias in each individual institute and the underlying feature statistics characterized by all participating institutes. The added-on VFDA consistently yielded marked improvements over six advanced FL methods on both 3D brain tumor and cardiac segmentation.Comment: 28th biennial international conference on Information Processing in Medical Imaging (IPMI 2023): Oral Pape

An investigation of siloxane cross-linked hydroxyapatite–gelatin/copolymer composites for potential orthopedic applications

Causes of bone deficiency are numerous, but biomimetic alloplastic grafts provide an alternative to repair tissue naturally. Previously, a hydroxyapatite-gelatin modified siloxane (HAp-Gemosil) composite was prepared by cross-linking (N, N′-bis[(3-trimethoxysilyl)propyl]ethylene diamine (enTMOS) around the HAp-Gel nanocomposite particles, to mimic the natural composition and properties of bone. However, the tensile strength remained too low for many orthopedic applications. It was hypothesized that incorporating a polymer chain into the composite could help improve long range interaction. Furthermore, designing this polymer to interact with the enTMOS siloxane cross-linked matrix would provide improved adhesion between the polymer and the ceramic composite, and improve mechanical properties. To this end, copolymers of L-Lactide (LLA), and a novel alkyne derivatized trimethylene carbonate, propargyl carbonate (PC), were synthesized. Incorporation of PC during copolymerization affects properties of copolymers such as molecular weight, Tg, and % PC incorporation. More importantly, PC monomers bear a synthetic handle, allowing copolymers to undergo post-polymerization functionalization with graft monomers to specifically tailor the properties of the final composite. For our investigation, P(LLA-co-PC) copolymers were functionalized by an azido-silane (AS) via copper catalyzed azide-alkyne cycloaddition (CuAAC) through terminal alkyne on PC monomers. The new functionalized polymer, P(LLA-co-PC)(AS) was blended with HAp-Gemosil, with the azido-silane linking the copolymer to the silsesquioxane matrix within the final composite

Permutation-invariant Feature Restructuring for Correlation-aware Image Set-based Recognition

We consider the problem of comparing the similarity of image sets with variable-quantity, quality and un-ordered heterogeneous images. We use feature restructuring to exploit the correlations of both inner$\&$inter-set images. Specifically, the residual self-attention can effectively restructure the features using the other features within a set to emphasize the discriminative images and eliminate the redundancy. Then, a sparse/collaborative learning-based dependency-guided representation scheme reconstructs the probe features conditional to the gallery features in order to adaptively align the two sets. This enables our framework to be compatible with both verification and open-set identification. We show that the parametric self-attention network and non-parametric dictionary learning can be trained end-to-end by a unified alternative optimization scheme, and that the full framework is permutation-invariant. In the numerical experiments we conducted, our method achieves top performance on competitive image set/video-based face recognition and person re-identification benchmarks.Comment: Accepted to ICCV 201

Levonorgestrel-releasing intrauterine system vs. usual medical treatment for menorrhagia: An economic evaluation alongside a randomised controlled trial

Objective: To undertake an economic evaluation alongside the largest randomised controlled trial comparing Levonorgestrel-releasing intrauterine device ('LNG-IUS') and usual medical treatment for women with menorrhagia in primary care; and compare the cost-effectiveness findings using two alternative measures of quality of life. Methods: 571 women with menorrhagia from 63 UK centres were randomised between February 2005 and July 2009. Women were randomised to having a LNG-IUS fitted, or usual medical treatment, after discussing with their general practitioner their contraceptive needs or desire to avoid hormonal treatment. The treatment was specified prior to randomisation. For the economic evaluation we developed a state transition (Markov) model with a 24 month follow-up. The model structure was informed by the trial women's pathway and clinical experts. The economic evaluation adopted a UK National Health Service perspective and was based on an outcome of incremental cost per Quality Adjusted Life Year (QALY) estimated using both EQ-5D and SF-6D. Results: Using EQ-5D, LNG-IUS was the most cost-effective treatment for menorrhagia. LNG-IUS costs £100 more than usual medical treatment but generated 0.07 more QALYs. The incremental cost-effectiveness ratio for LNG-IUS compared to usual medical treatment was £1600 per additional QALY. Using SF-6D, usual medical treatment was the most cost-effective treatment. Usual medical treatment was both less costly (£100) and generated 0.002 more QALYs. Conclusion: Impact on quality of life is the primary indicator of treatment success in menorrhagia. However, the most costeffective treatment differs depending on the quality of life measure used to estimate the QALY. Under UK guidelines LNG-IUS would be the recommended treatment for menorrhagia. This study demonstrates that the appropriate valuation of outcomes in menorrhagia is crucial. Copyright: © 2014 Sanghera et al

Entangled two cavity modes preparation via a two-photon process

We propose a scheme for entangling two field modes in two high-Q optical cavities. Making use of a virtual two-photon process, our scheme achieves maximally entangled states without any real transitions of atomic internal states, hence it is immune to the atomic decay.Comment: 4 pages, latex, 7 figure