A Successful Broker Agent for Power TAC

The Power TAC simulates a smart grid energy market. In this simulation, broker agents compete for customers on a tariff market and trade energy on a wholesale market. It provides a platform for testing strategies of broker agents against other strategies. In this paper we describe the strategies of our broker agent. Amongst others, due to a beneficial trading technique related to equilibria in continuous auctions on the wholesale market and a strategy inspired by Tit-for-Tat in the Iterated Prisoner's Dilemma game on the tariff market, our broker ended second in the 2013 Power TAC

The value of online information for demand response in Walrasian electricity markets

textabstractSmart energy systems integrate renewables and demand response. Most European electricity markets coordinate the resulting time-varying flexibility in demand and supply by organising day-ahead trade with Walrasian mechanisms, using simultaneous call auctions and sealed bids. These mechanisms give bidders no information on each other's values and flexibilities until after clearing. In this paper we simulate two alternative day-ahead market mechanisms which share information, such that bidders obtain a better position before entering the intraday market. One mechanism uses an ascending shared market price signal rather than sealed bids. The other auctions off future timeslots consecutively rather than simultaneously. We perform a case study on 400 households with electric vehicles, either with or without volatile wind generation. Results show that a price-taking flexible consumer can obtain higher utility in the market with simultaneous ascending-price auctions, because online price information reduces uncertainty over available energy and prices

Deep-learning automated quantification of longitudinal OCT scans demonstrates reduced RPE loss rate, preservation of intact macular area and predictive value of isolated photoreceptor degeneration in geographic atrophy patients receiving C3 inhibition treatment

OBJECTIVE: To evaluate the role of automated optical coherence tomography (OCT) segmentation, using a validated deep-learning model, for assessing the effect of C3 inhibition on the area of geographic atrophy (GA); the constituent features of GA on OCT (photoreceptor degeneration (PRD), retinal pigment epithelium (RPE) loss and hypertransmission); and the area of unaffected healthy macula.To identify OCT predictive biomarkers for GA growth. METHODS: Post hoc analysis of the FILLY trial using a deep-learning model for spectral domain OCT (SD-OCT) autosegmentation. 246 patients were randomised 1:1:1 into pegcetacoplan monthly (PM), pegcetacoplan every other month (PEOM) and sham treatment (pooled) for 12 months of treatment and 6 months of therapy-free monitoring. Only participants with Heidelberg SD-OCT were included (n=197, single eye per participant).The primary efficacy endpoint was the square root transformed change in area of GA as complete RPE and outer retinal atrophy (cRORA) in each treatment arm at 12 months, with secondary endpoints including RPE loss, hypertransmission, PRD and intact macular area. RESULTS: Eyes treated PM showed significantly slower mean change of cRORA progression at 12 and 18 months (0.151 and 0.277 mm, p=0.0039; 0.251 and 0.396 mm, p=0.039, respectively) and RPE loss (0.147 and 0.287 mm, p=0.0008; 0.242 and 0.410 mm, p=0.00809). PEOM showed significantly slower mean change of RPE loss compared with sham at 12 months (p=0.0313). Intact macular areas were preserved in PM compared with sham at 12 and 18 months (p=0.0095 and p=0.044). PRD in isolation and intact macula areas was predictive of reduced cRORA growth at 12 months (coefficient 0.0195, p=0.01 and 0.00752, p=0.02, respectively) CONCLUSION: The OCT evidence suggests that pegcetacoplan slows progression of cRORA overall and RPE loss specifically while protecting the remaining photoreceptors and slowing the progression of healthy retina to iRORA

Evaluating the Effects of C3 Inhibition on Geographic Atrophy Progression from Deep-Learning OCT Quantification:A Split-Person Study

Introduction: To evaluate the effect pegcetacoplan, a C3 and C3b inhibitor, on the rate of progression of geographic atrophy (GA) as assessed by spectral domain optical coherence tomography (SD-OCT) using a split-person study design and deep-learning quantification. Methods: A post hoc analysis of phase 2 FILLY trial data comparing study (treated monthly, treated every other month and sham-treated) and fellow (untreated) eyes in a split-person study design was performed. This analysis included 288 eyes from 144 patients with bilateral GA from the FILLY phase 2 trial (Clinical Trials identifier: NCT02503332). Only patients with bilateral GA and without evidence of choroidal neovascularisation in either eye were included. Patient study eyes were treated with sham injections or with pegcetacoplan monthly (PM) or every other month (PEOM) for 12 months. SD-OCT scans of study and fellow eyes taken at baseline and 12 months were used for the analysis. The main outcomes were the annual change in the area of retinal pigment epithelial and outer retinal atrophy (RORA), its constituent features (photoreceptor degeneration [PRD], retinal pigment epithelium [RPE] loss, hypertransmission) and intact macula as compared to the untreated fellow eye. Results: Annual GA growth was reduced in eyes treated with PM versus untreated fellow eyes for OCT features, including RORA (study eye 0.792 vs. fellow eye 1.13 mm2; P = 0.003), PRD (0.739 vs. 1.23 mm2; P = 0.015), RPE-loss (0.789 vs. 1.17 mm2; P = 0.007) and intact macula (− 0.735 vs. − 1.29 mm2; P = 0.011). Similar (but not statistically significant) trends were observed with the PEOM treatment or when GA was quantified with fundus autofluorescence (FAF). The sham treatment demonstrated no effect. Pearson correlation coefficients showed concordance in the enlargement rate of GA between the study and fellow eyes in the sham (R = 0.64) and PEOM (R = 0.68) groups, but not in the PM group (R = 0.21). Conclusions: Pegcetacoplan-treated eyes demonstrated a reduction in spatial GA progression compared to their untreated counterparts. This effect was more evident on OCT than with FAF. Trial Registration: Clinical Trials identifier: NCT02503332.</p

Evaluating the Effects of C3 Inhibition on Geographic Atrophy Progression from Deep-Learning OCT Quantification: A Split-Person Study

INTRODUCTION: To evaluate the effect pegcetacoplan, a C3 and C3b inhibitor, on the rate of progression of geographic atrophy (GA) as assessed by spectral domain optical coherence tomography (SD-OCT) using a split-person study design and deep-learning quantification. METHODS: A post hoc analysis of phase 2 FILLY trial data comparing study (treated monthly, treated every other month and sham-treated) and fellow (untreated) eyes in a split-person study design was performed. This analysis included 288 eyes from 144 patients with bilateral GA from the FILLY phase 2 trial (Clinical Trials identifier: NCT02503332). Only patients with bilateral GA and without evidence of choroidal neovascularisation in either eye were included. Patient study eyes were treated with sham injections or with pegcetacoplan monthly (PM) or every other month (PEOM) for 12 months. SD-OCT scans of study and fellow eyes taken at baseline and 12 months were used for the analysis. The main outcomes were the annual change in the area of retinal pigment epithelial and outer retinal atrophy (RORA), its constituent features (photoreceptor degeneration [PRD], retinal pigment epithelium [RPE] loss, hypertransmission) and intact macula as compared to the untreated fellow eye. RESULTS: Annual GA growth was reduced in eyes treated with PM versus untreated fellow eyes for OCT features, including RORA (study eye 0.792 vs. fellow eye 1.13 mm2; P = 0.003), PRD (0.739 vs. 1.23 mm2; P = 0.015), RPE-loss (0.789 vs. 1.17 mm2; P = 0.007) and intact macula (- 0.735 vs. - 1.29 mm2; P = 0.011). Similar (but not statistically significant) trends were observed with the PEOM treatment or when GA was quantified with fundus autofluorescence (FAF). The sham treatment demonstrated no effect. Pearson correlation coefficients showed concordance in the enlargement rate of GA between the study and fellow eyes in the sham (R = 0.64) and PEOM (R = 0.68) groups, but not in the PM group (R = 0.21). CONCLUSIONS: Pegcetacoplan-treated eyes demonstrated a reduction in spatial GA progression compared to their untreated counterparts. This effect was more evident on OCT than with FAF. TRIAL REGISTRATION: Clinical Trials identifier: NCT02503332

Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors

Adversarial attacks are considered a potentially serious security threat for machine learning systems. Medical image analysis (MedIA) systems have recently been argued to be vulnerable to adversarial attacks due to strong financial incentives and the associated technological infrastructure. In this paper, we study previously unexplored factors affecting adversarial attack vulnerability of deep learning MedIA systems in three medical domains: ophthalmology, radiology, and pathology. We focus on adversarial black-box settings, in which the attacker does not have full access to the target model and usually uses another model, commonly referred to as surrogate model, to craft adversarial examples. We consider this to be the most realistic scenario for MedIA systems. Firstly, we study the effect of weight initialization (ImageNet vs. random) on the transferability of adversarial attacks from the surrogate model to the target model. Secondly, we study the influence of differences in development data between target and surrogate models. We further study the interaction of weight initialization and data differences with differences in model architecture. All experiments were done with a perturbation degree tuned to ensure maximal transferability at minimal visual perceptibility of the attacks. Our experiments show that pre-training may dramatically increase the transferability of adversarial examples, even when the target and surrogate's architectures are different: the larger the performance gain using pre-training, the larger the transferability. Differences in the development data between target and surrogate models considerably decrease the performance of the attack; this decrease is further amplified by difference in the model architecture. We believe these factors should be considered when developing security-critical MedIA systems planned to be deployed in clinical practice.Comment: First three authors contributed equall

A Deep Learning Model for Segmentation of Geographic Atrophy to Study Its Long-Term Natural History

__Purpose:__ To develop and validate a deep learning model for the automatic segmentation of geographic atrophy (GA) using color fundus images (CFIs) and its application to study the growth rate of GA. __Design:__ Prospective, multicenter, natural history study with up to 15 years of follow-up. __Participants:__ Four hundred nine CFIs of 238 eyes with GA from the Rotterdam Study (RS) and Blue Mountain Eye Study (BMES) for model development, and 3589 CFIs of 376 eyes from the Age-Related Eye Disease Study (AREDS) for analysis of GA growth rate. __Methods:__ A deep learning model based on an ensemble of encoder–decoder architectures was implemented and optimized for the segmentation of GA in CFIs. Four experienced graders delineated, in consensus, GA in CFIs from the RS and BMES. These manual delineations were used to evaluate the segmentation model using 5-fold cross-validation. The model was applied further to CFIs from the AREDS to study the growth rate of GA. Linear regression analysis was used to study associations between structural biomarkers at baseline and the GA growth rate. A general estimate of the progression of GA area over time was made by combining growth rates of all eyes with GA from the AREDS set. __Main Outcome Measures:__ Automatically segmented GA and GA growth rate. __Results:__ The model obtained an average Dice coefficient of 0.72±0.26 on the BMES and RS set while comparing the automatically segmented GA area with the graders’ manual delineations. An intraclass correlation coefficient of 0.83 was reached between the automatically estimated GA area and the graders’ consensus measures. Nine automatically calculated structural biomarkers (area, filled area, convex area, convex solidity, eccentricity, roundness, foveal involvement, perimeter, and circularity) were significantly associated with growth rate. Combining all growth rates indicated that GA area grows quadratically up to an area of approximately 12 mm2, after which growth rate stabilizes or decreases. __Conclusions:__ The deep learning model allowed for fully automatic and robust segmentation of GA on CFIs. These segmentations can be used to extract structural characteristics of GA that predict its growth rate

SynthEye: Investigating the Impact of Synthetic Data on Artificial Intelligence-assisted Gene Diagnosis of Inherited Retinal Disease

PURPOSE: Rare disease diagnosis is challenging in medical image-based artificial intelligence due to a natural class imbalance in datasets, leading to biased prediction models. Inherited retinal diseases (IRDs) are a research domain that particularly faces this issue. This study investigates the applicability of synthetic data in improving artificial intelligence-enabled diagnosis of IRDs using generative adversarial networks (GANs). DESIGN: Diagnostic study of gene-labeled fundus autofluorescence (FAF) IRD images using deep learning. PARTICIPANTS: Moorfields Eye Hospital (MEH) dataset of 15 692 FAF images obtained from 1800 patients with confirmed genetic diagnosis of 1 of 36 IRD genes. METHODS: A StyleGAN2 model is trained on the IRD dataset to generate 512 × 512 resolution images. Convolutional neural networks are trained for classification using different synthetically augmented datasets, including real IRD images plus 1800 and 3600 synthetic images, and a fully rebalanced dataset. We also perform an experiment with only synthetic data. All models are compared against a baseline convolutional neural network trained only on real data. MAIN OUTCOME MEASURES: We evaluated synthetic data quality using a Visual Turing Test conducted with 4 ophthalmologists from MEH. Synthetic and real images were compared using feature space visualization, similarity analysis to detect memorized images, and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score for no-reference-based quality evaluation. Convolutional neural network diagnostic performance was determined on a held-out test set using the area under the receiver operating characteristic curve (AUROC) and Cohen's Kappa (κ). RESULTS: An average true recognition rate of 63% and fake recognition rate of 47% was obtained from the Visual Turing Test. Thus, a considerable proportion of the synthetic images were classified as real by clinical experts. Similarity analysis showed that the synthetic images were not copies of the real images, indicating that copied real images, meaning the GAN was able to generalize. However, BRISQUE score analysis indicated that synthetic images were of significantly lower quality overall than real images (P < 0.05). Comparing the rebalanced model (RB) with the baseline (R), no significant change in the average AUROC and κ was found (R-AUROC = 0.86[0.85-88], RB-AUROC = 0.88[0.86-0.89], R-k = 0.51[0.49-0.53], and RB-k = 0.52[0.50-0.54]). The synthetic data trained model (S) achieved similar performance as the baseline (S-AUROC = 0.86[0.85-87], S-k = 0.48[0.46-0.50]). CONCLUSIONS: Synthetic generation of realistic IRD FAF images is feasible. Synthetic data augmentation does not deliver improvements in classification performance. However, synthetic data alone deliver a similar performance as real data, and hence may be useful as a proxy to real data. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references