Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images.

Assessing the degree of disease severity in biomedical images is a task similar to standard classification but constrained by an underlying structure in the label space. Such a structure reflects the monotonic relationship between different disease grades. In this paper, we propose a straightforward approach to enforce this constraint for the task of predicting Diabetic Retinopathy (DR) severity from eye fundus images based on the well-known notion of Cost-Sensitive classification. We expand standard classification losses with an extra term that acts as a regularizer, imposing greater penalties on predicted grades when they are farther away from the true grade associated to a particular image. Furthermore, we show how to adapt our method to the modelling of label noise in each of the sub-problems associated to DR grading, an approach we refer to as Atomic Sub-Task modeling. This yields models that can implicitly take into account the inherent noise present in DR grade annotations. Our experimental analysis on several public datasets reveals that, when a standard Convolutional Neural Network is trained using this simple strategy, improvements of 3- 5% of quadratic-weighted kappa scores can be achieved at a negligible computational cost. Code to reproduce our results is released at github.com/agaldran/cost_sensitive_loss_classification

Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study

10.1016/S2589-7500(20)30060-1The Lancet Digital Health25e240-e24

Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy

10.1038/s41746-020-0247-1npj Digital Medicine314

Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study

10.1016/S2589-7500(19)30004-4The Lancet Digital Health11e35-e4

Posterior scleral birefringence measured by triple-input polarization-sensitive imaging as a biomarker of myopia progression

In myopic eyes, pathological remodelling of collagen in the posterior sclera has mostly been observed ex vivo. Here we report the development of triple-input polarization-sensitive optical coherence tomography (OCT) for measuring posterior scleral birefringence. In guinea pigs and humans, the technique offers superior imaging sensitivities and accuracies than dual-input polarization-sensitive OCT. In 8-week-long studies with young guinea pigs, scleral birefringence was positively correlated with spherical equivalent refractive errors and predicted the onset of myopia. In a cross-sectional study involving adult individuals, scleral birefringence was associated with myopia status and negatively correlated with refractive errors. Triple-input polarization-sensitive OCT may help establish posterior scleral birefringence as a non-invasive biomarker for assessing the progression of myopia.Agency for Science, Technology and Research (A*STAR)Nanyang Technological UniversityNational Medical Research Council (NMRC)National Research Foundation (NRF)Published versionThis work was funded by grants from the Industry Alignment Fund - Industry Collaboration Projects (IAF-ICP) Grant (I1901E0038, L.S., Q.V.H., A.W.C., R.P.N., V.A.B., M.A. and S.-M.S.) and Johnson & Johnson Vision. We also acknowledge the support of the National Medical Research Council (CG/C010A/2017_SERI, L.S.; OFLCG/004c/2018-00, L.S.; MOH-000249-00, J. Chua; MOH-000647-00, L.S.; MOH-001001-00, L.S.; MOH-001015-00, L.S.; MOH-000500-00, L.S.; MOH-000707-00, L.S.; MOH-001072-06, L.S.; NMRC/CSIRG/MOH-000531/2021, Q.V.H.); the National Research Foundation Singapore (NRF2019-THE002-0006, L.S. and NRF-CRP24-2020-0001, L.S.), A*STAR (A20H4b0141, L.S., J. Chua), the Singapore Eye Research Institute & Nanyang Technological University (SERI-NTU Advanced Ocular Engineering (STANCE) Program, L.S.), the SERI-Lee Foundation (LF1019-1, J. Chua), the US National Institutes of Health (P41EB-015903, M.V. and R01 EY023966, I.A.S.), the EU (H2020-MSCA-IF-2019 program 894325, M.L.) and the Singapore Eye Research Institute & National University of Singapore ASPIRE Program (NUHSRO/2022/038/Startup/08, R.P.N.)

THE NEXT LINEAR COLLIDER DAMPING RING COMPLEX ∗

We report progress on the design of the Next Linear Collider (NLC) Damping Rings complex (DRC) [1]. The purpose of the DRC is to provide 120 Hz, low emittance electron and positron bunch trains to the NLC linacs [2]. It consists of two 1.98 GeV main damping rings, one positron pre-damping ring, two pairs of bunch length and energy compressor systems and interconnecting transport lines. The 2 main damping rings store up to 0.8 amp in 3 trains of 95 bunches each and have normalized extracted beam emittances γεx = 3 μm-rad and γεy = 0.03 μm-rad. The preliminary optical design, performance specifications and tolerances are given in [1]. Key subsystems include 1) the 714 MHz RF system [3], 2) the 60 ns risetime injection / extraction pulsed kicker magnets [4], 3) the 44 m wiggler magnet system, 4) the arc and wiggler vacuum system, 5) the radiation management system, 6) the beam diagnostic instrumentation, 7) special systems used for downstream machine protection and 8) feedback-based stabilization systems. Experience at the SLAC Linear Collider has shown that the NLC damping rings will have a pivotal role in the operation of the high power linacs. The ring dynamics and instabilities will in part determine the design choices made for the NLC machine protection system. This paper includes a summary overview of the main ring design and key subsystem components.