Incremental Detection Research Articles

A multi-sensor fused incremental detection model for blade crack with cross-attention mechanism and Dempster-Shafer evidence theory

A multi-sensor fused incremental detection model for blade crack with cross-attention mechanism and Dempster-Shafer evidence theory

Compaction quality assessment of highway using roller-integrated positive maximum kinetic energy increment detection technique

During conventional highway construction, quality control and acceptance (QC/QA) are based on limited spot tests of material density at random locations which may not comprehensively represent the entire compacted area and are vulnerable to potential biases. Based on roller-integrated positive maximum kinetic energy increment detection technique with real-time kinematic global positioning systems, this research adopted kinetic energy compaction value (KECV) as a real-time monitoring index for the highway compaction quality, and subsequently proposed an KECV-based assessment method to estimate the compaction quality of subgrade and pavement materials. Additionally, a global interpolation method (Green spline) was adopted to obtain estimates for both KECVs and compaction quality at any location on the work area, enabling the analysis of the passing rate of compaction quality for the entire work area. A case study on the Hengyong highway project in China indicates a highly linear correlation between the KECV and the compaction parameters, the compactness of LLL silt, and the relative density of cement-stabilized macadam; therefore, it can serve as a reliable index for monitoring compaction quality. Assessment of compaction quality on the entire work area can be fast and continuously achieved using the proposed method, which can effectively overcome the above limitation of conventional testing, timely feedback compaction information to avoid quality defects, and availably improve the construction quality of highways.

Continual Learning for SAR Target Incremental Detection via Predicted Location Probability Representation and Proposal Selection

Continual Learning for SAR Target Incremental Detection via Predicted Location Probability Representation and Proposal Selection

Incremental Detection of Hyperspectral Targets in Consistent Scenes with Continuous Learning

Incremental Detection of Hyperspectral Targets in Consistent Scenes with Continuous Learning

Contributed Session II: The relationship between temporal summation at detection threshold and fixational eye movements.

We studied the relationship between the threshold temporal summation of increment pulses and fixational eye-movements. Six participants completed a 2AFC increment detection task. Stimuli were 0.16 x 2.2 arcmin increments of 543 nm light presented via an AOSLO with a 60 Hz frame rate. Stimuli for temporal integration were two single frame presentations with a 16 ms (consecutive frames), 33 ms, 100 ms, or 300 ms inter-stimulus interval (ISI). Data were also collected for increments presented on a single frame. Stimuli were presented in either world-fixed coordinates (natural retinal image motion) or were stabilised on the retina. There were large differences in overall sensitivity across individuals, but the time-course of performance change with ISI was similar across participants. Thresholds for ISI=33 ms were close to performance with two consecutive frames, suggesting complete summation of light energy; whereas thresholds for ISI=300 ms were closer to the single-frame case, suggesting limited summation; and thresholds for ISI=100 ms were intermediate, suggesting residual summation. The effect of ISI on threshold was similar for stabilised stimuli and natural viewing, but there was a small trend towards lower thresholds for stabilised stimuli at short ISI and vice-versa at long ISI. We plan to present our results in the context of an ideal observer calculation that may clarify how the initial visual encoding, including temporal summation within cones, shapes performance.

Performance Evaluation of an Improved HBsAg Assay (HBsAg NEXT) for the Detection of HBsAg Levels.

Objectives Detection of hepatitis B surface antigen (HBsAg) plays an important role in the screening and diagnosis of hepatitis B virus (HBV) infections. There is a need of highly sensitive assays with an improved lower limit of detection (LoD). Here, we evaluate the performance characteristics of the HBsAg NEXT (HBsAg new) assay in the detection of HBsAg in clinical samples. Materials and Methods This was a cross-sectional study conducted at a tertiary care liver center in North India. The study included 439 clinical samples. The HBsAg new assay was compared to the conventional chemiluminescence-based assay (HBsAg old assay, Architect, Abbott Diagnostics, United States). The analytical sensitivity of the HBsAg new assay was evaluated by checking its performance with the second World Health Organization (WHO) international standards for HBsAg. Results Out of 439 blood samples that were retrieved from the departmental repository stored at -80°C, 100 samples were positive and 339 samples were negative for HBsAg as per the HBsAg old assay. The HBsAg new assay showed incremental detection of HBsAg in 11 additional samples. Out of these, 5 samples were confirmed as occult HBV infection and the remaining 6 were classified as "exposed-to-virus" samples (HBV core total antibody-positive). The HBsAg new assay demonstrated a high positive significant correlation with the HBsAg old assay ( r = 0.881, p -value < 0.001). The HBsAg new assay could effectively detect the second WHO international standards to the level of 0.0033 IU/mL. Conclusion The HBsAg NEXT assay is a highly sensitive assay with an improved lower LoD.

Development of a multiphase variable mass flow model and its application in well control for variable gradient managed pressure drilling

A multiphase flow model is an important foundation for well control simulation. In this paper, a multiphase variable mass flow model considering sudden density change was developed for variable gradient managed pressure drilling (MPD) and it was solved by finite difference method (FDM). Further, the model was used to simulate the whole process of well control based on constant bottom-hole pressure for variable gradient MPD. In this way, the advantages of the new gas kick detection method compared with the mud pit increment detection method and the variation laws of key parameters in the whole well control process were investigated. The results indicate that under the condition of small gas rate, the new gas kick detection method is better than the mud pit increment detection method because of its time savings. In the process of gas discharge, the wellbore pressure fluctuates due to the influence of gas expansion, sudden density changes at the separator position and annulus diameter changes at the casing shoe and mud line. The time when the back pressure reaches the maximum lags behind the time when the gas front reaches the wellhead. The mud pit increment increase rate when the gas front reaches the separator position is significantly higher than that in the early stage of gas kick detection.

Incremental Detection Rate of Dysplasia and Sessile Serrated Polyps/Adenomas Using Narrow-Band Imaging and Dye Spray Chromoendoscopy in Addition to High-Definition Endoscopy in Patients with Long-Standing Extensive Ulcerative Colitis: Segmental Tandem Endoscopic Study.

High-definition (HD) endoscopy is recommended in surveillance colonoscopy for detecting dysplasia in patients with ulcerative colitis (UC). Dye-spray chromoendoscopy (DCE) and narrow-band imaging (NBI) are often used as adjunctive techniques of white-light endoscopy (WLE) in real-world practice. However, the incremental detection ability of DCE and NBI added to HD-WLE for dysplasia and serrated lesions has not yet been evaluated using tandem endoscopy in patients with long-standing extensive UC. We enrolled patients with extensive UC for >8 years who were in clinical remission (partial Mayo score < 2) at the Samsung Medical Center in Seoul, Republic of Korea. HD-WLE was performed first. Subsequently, HD-NBI and HD-DCE with indigo carmine were performed using the segmental tandem colonoscopy technique. A total of 40 patients were eligible, and data obtained from 33 patients were analyzed. The incremental detection rates (IDRs) for dysplasia and serrated lesions were calculated. HD-WLE detected three dysplasia and five sessile serrated adenomas/polyps (SSAs/Ps). HD-NBI and HD-DCE did not detect additional dysplasia (IDR = 0%; 95% confidence interval (CI): 0-56.2%). HD-NBI identified one missed SSA/P (IDR = 7.7%; 95% CI: 1.4-33.3%), and HD-DCE detected seven missed SSAs/Ps (IDR = 53.9%; 95% CI: 29.1-76.8%). Logistic regression found that HD-DCE increased the detection of SSAs/Ps compared to HD-WLE and/or HD-NBI (odds ratio (OR) = 3.16, 95% CI: 0.83-11.92, p = 0.08). DCE in addition to HD-WLE improved the detection of SSAs/Ps, but not dysplasia, in patients with long-standing extensive UC.

A Pain in the Neck: Lessons Learnt from Genetic Testing in Fetuses Detected with Nuchal Fluid Collections, Increased Nuchal Translucency versus Cystic Hygroma—Systematic Review of the Literature, Meta-Analysis and Case Series

Fetal Nuchal fluid collections can manifest with two distinct presentations attributable to the same phenotypic spectrum: increased nuchal translucency (iNT) and cystic hygroma. The prenatal detection of these findings should prompt an accurate assessment through genetic counseling and testing, including karyotype, chromosomal microarray analysis (CMA) and multigene RASopathy panel. We performed a systematic review of the literature and meta-analysis, to calculate diagnostic yields of genetic testing in fetuses with iNT and cystic hygroma. We compared the results with a cohort of 96 fetuses with these isolated findings. Fetuses with isolated NT ≥ 2.5 mm showed karyotype anomalies in 22.76% of cases and CMA presented an incremental detection rate of 2.35%. Fetuses with isolated NT ≥ 3 mm presented aneuploidies in 14.36% of cases and CMA had an incremental detection rate of 3.89%. When the isolated NT measured at least 3.5 mm the diagnostic yield of karyotyping was 34.35%, the incremental CMA detection rate was 4.1%, the incremental diagnostic rate of the RASopathy panel was 1.44% and it was 2.44% for exome sequencing. Interestingly, CMA presents a considerable diagnostic yield in the group of fetuses with NT ≥ 3.5 mm. Similarly, exome sequencing appears to show promising results and could be considered after a negative CMA result.

Firewall Anomaly Detection Based on Double Decision Tree

To solve the problems regarding how to detect anomalous rules with an asymmetric structure, which leads to the firewall not being able to control the packets in and out according to the administrator’s idea, and how to carry out an incremental detection efficiently when the new rules are added, anomaly detection algorithms based on an asymmetric double decision tree were considered. We considered the packet filter, the most common and used type of First Matching Rule, for the practical decision space of each rule and the whole policy. We adopted, based on the asymmetric double decision tree detection model, the policy equivalent decision tree and the policy decision tree of anomalies. Therefore, we can separate the policy’s effective decision space and the anomalous decision space. Using the separated decision trees can realize the optimization of the original policy and the faster incremental detection when adding new rules and generating a detailed report. The simulation results demonstrate that the proposed algorithms are superior to the other decision tree algorithms in detection speed and can achieve incremental detection. The results demonstrate that our approach can save about 33% of the time for complete detection compared with the other approaches, and the time of incremental anomaly detection compared to complete detection is about 90% of the time saved in a complex policy.

DIODE: Dilatable Incremental Object Detection

To accommodate rapid changes in the real world, the cognition system of humans is capable of continually learning concepts. On the contrary, conventional deep learning models lack this capability of preserving previously learned knowledge. When a neural network is fine-tuned to learn new tasks, its performance on previously trained tasks will significantly deteriorate. Many recent works on incremental object detection tackle this problem by introducing advanced regularization. Although these methods have shown promising results, the benefits are often short-lived after the first incremental step. Under multi-step incremental learning, the trade-off between old knowledge preserving and new task learning becomes progressively more severe. Thus, the performance of regularization-based incremental object detectors gradually decays for subsequent learning steps. In this paper, we aim to alleviate this performance decay on multi-step incremental detection tasks by proposing a dilatable incremental object detector (DIODE). For the task-shared parameters, our method adaptively penalizes the changes of important weights for previous tasks. At the same time, the structure of the model is dilated or expanded by a limited number of task-specific parameters to promote new task learning. Extensive experiments on PASCAL VOC and COCO datasets demonstrate substantial improvements over the state-of-the-art methods. Notably, compared with the state-of-the-art methods, our method achieves up to 6.0% performance improvement by increasing the number of parameters by just 1.2% for each newly learned task.

A Class-Incremental Detection Method of Remote Sensing Images Based on Selective Distillation

With the rapid development of remote sensing technology and the growing demand for applications, the classical deep learning-based object detection model is bottlenecked in processing incremental data, especially in the increasing classes of detected objects. It requires models to sequentially learn new classes of objects based on the current model, while preserving old categories-related knowledge. Existing class-incremental detection methods achieve this goal mainly by constraining the optimization trajectory in the feature of output space. However, these works neglect the case where the previously learned background is a new category to learn, resulting in performance degradation in the new category because of the conflict between remaining the background-related knowledge or updating the background-related knowledge. This paper proposes a novel class-incremental detection method incorporated with the teacher-student architecture and the selective distillation (SDCID) strategy. Specifically, it is the asymmetry architecture, i.e., the teacher network temporarily stores historical knowledge of previously learned objects, and the student network integrates historical knowledge from the teacher network with the newly learned object-related knowledge, respectively. This asymmetry architecture reveals the significance of the distinct representation of history knowledge and new knowledge in incremental detection. Furthermore, SDCID selectively masks the shared subobject of new images to learn and previously learned background, while learning new categories of images and then transfers the classification results of the student model to the background class following the judgment model of the teacher model. This manner avoids interferences between the background category-related knowledge from a teacher model and the learning of other new classes of objects. In addition, we proposed a new incremental learning evaluation metric, C-SP, to comprehensively evaluate the incremental learning stability and plasticity performance. We verified the proposed method on two object detection datasets of remote sensing images, i.e., DIOR and DOTA. The experience results in accuracy and C-SP suggest that the proposed method surpasses existing class-incremental detection methods. We further analyzed the influence of the mask component in our method and the hyper-parameters sensitive to our method.

MTOR-Mediated Autophagy Regulates Fumonisin B1-Induced Intestinal Inflammation via Pyroptosis In Vivo and In Vitro.

Fumonisin B1 (FB1) is a fungal metabolite, which has an incremental detection rate in grains and feed worldwide. The nucleotide-binding oligomerization domain-like pyrin domain containing protein 3 (NLRP3) inflammasome is a critical element in pyroptosis activation, which participates in regulating enteritis. Meanwhile, autophagy is also engaged in intestinal inflammation. However, the function of pyroptosis and autophagy in FB1-mediated enterotoxicity remains unclear. In this study, we explored the effects of FB1 on enteritis and the underlying mechanism in vivo and in vitro. Our data showed that FB1 exposure damaged the intestinal epithelium and promoted the secretion of inflammatory cytokines. Meanwhile, FB1 exposure significantly upregulated the expression of pyroptosis-related genes. Then, MCC950, an inhibitor of NLRP3, significantly blocked FB1-induced pyroptosis in IPEC-J2 cells. In addition, FB1 treatment elevated the levels of autophagy. Moreover, the phosphorylation of the mammalian target of rapamycin (mTOR), an upstream protein of the autophagy pathway, was inhibited by FB1 exposure. Notably, rapamycin, an inhibitor of mTOR, instead of MHY1485, an agonist of mTOR, could ameliorate FB1-induced intestinal inflammatory injury and inhibit the upregulation of pyroptosis-related genes. In summary, we demonstrated that autophagy exhibited a protective effect against NLRP3 inflammasome-dependent pyroptosis on FB1-induced enteritis. Our data clarify a favorable protective role for the activation of autophagy in FB1 poisoning.

E-ware: a big data system for the incremental discovery of spatio-temporal events from microblogs.

Event detection from social media aims at extracting specific or generic unusual happenings, such as, family reunions, earthquakes, and disease outbreaks, among others. This paper introduces a new perspective for the incremental extraction and clustering of social events from big social data streams. We present ‘E-ware’, a scalable and efficient big data platform that integrates data stream and geospatial processing tools for the incremental extraction and dissemination of spatio-temporal events. We introduce a pure incremental approach for event discovery, by developing unsupervised machine learning and NLP algorithms and by computing events’ lifetime and spatial spanning. Our incremental clustering technique employs temporal sliding windows, in order to update the discovered topic clusters with the upcoming social streams (i.e., tweets). The system integrates an efficient spatio-temporal index for fast retrieval and updates of evolving event clusters. We conduct experiments over Twitter datasets to measure the effectiveness and efficiency of our system. The results demonstrate that E-ware has a major advantage for real-time incremental detection and tracking of events, both spatially and temporally. This leads to the development of unparalleled smart city applications, such as event-enriched trip planning, epidemic disease evolution, and proactive emergency management services.

Adaptation of a Public-Private Partnership Model for the Implementation of Minipool Nucleic Acid Testing for Screening Routine Blood Donations and Assay Evaluation.

Background Nucleic acid amplification testing (NAT) for the screening of blood donations is known to improve blood safety. The decision to initiate NAT requires careful deliberation of infrastructure, skilled manpower, and financial resources. This report outlines the initiative of the Government of Odisha to implement NAT screening in government blood banks in the state of Odisha, India, through public–private partnership (PPP) and evaluates the incremental yield of minipool NAT screening over serology testing of blood donations. Methods Blood donations collected between June 2016 and September 2018 were initially screened for HBV (HBsAg), HCV (anti-HCV), and HIV (anti-HIV-1 and HIV-2) by ELISA, and syphilis and malaria. Sero-nonreactive donations were further screened in pools of six by Roche cobas TaqScreen MPX test version 2.0 (MPX2) NAT. Results On screening 3,39,472 blood donations, 1.34% seroreactive donations were detected. In all, 847 NAT-reactive donations (0.26%): 693 HBV, 58 HCV, and 96 HIV were detected. The NAT yields were 1:386 overall, 1:472 for HBV, 1:5642 for HCV, and 1:3409 for HIV. Conclusion NAT testing using the highly sensitive MPX2 assay leads to incremental detection of TTIs over serology. Implementation of NAT along with serological testing in blood centers all over India will be an important step towards providing safe blood. Our study not only highlights the benefits of minipool NAT testing but also presents a scalable PPP model that can serve as a template for application across other states.

A Forest Point Cloud Real-Time Reconstruction Method with Single-Line Lidar Based on Visual–IMU Fusion

In order to accurately obtain tree growth information from a forest at low cost, this paper proposes a forest point cloud real-time reconstruction method with a single-line lidar based on visual–IMU fusion. We build a collection device based on a monocular camera, inertial measurement unit (IMU), and single-line lidar. Firstly, pose information is obtained using the nonlinear optimization real-time location method. Then, lidar data are projected to the world coordinates and interpolated to form a dense spatial point cloud. Finally, an incremental iterative point cloud loopback detection algorithm based on visual key frames is utilized to optimize the global point cloud and further improve precision. Experiments are conducted in a real forest. Compared with a reconstruction based on the Kalman filter, the root mean square error of the point cloud map decreases by 4.65%, and the time of each frame is 903 μs; therefore, the proposed method can realize real-time scene reconstruction in large-scale forests.

Incremental Detection of Severe Congenital Heart Disease by Fetal Echocardiography Following a Normal Second Trimester Ultrasound Scan in Québec, Canada.

Background:The benefit of fetal echocardiograms (FE) to detect severe congenital heart diseases (SCHD) in the setting of a normal second-trimester ultrasound is unclear. We aimed to assess whether the increase in SCHD detection rates when FE are performed for risk factors in the setting of a normal ultrasound was clinically significant to justify the resources needed.Methods:This is a multicenter, population-based, retrospective cohort study, including all singleton pregnancies and offspring in Quebec (Canada) between 2007 and 2015. Administrative health care data were linked with FE clinical data to gather information on prenatal diagnosis of CHD, indications for FE, outcomes of pregnancy and offspring, postnatal diagnosis of CHD, cardiac interventions, and causes of death. The difference between the sensitivity to detect SCHD with and without FE for risk factors was calculated using generalized estimating equations with a noninferiority margin of 5 percentage points.Results:A total of 688 247 singleton pregnancies were included, of which 30 263 had at least one FE. There were 1564 SCHD, including 1071 that were detected prenatally (68.5%). There were 12 210 FE performed for risk factors in the setting of a normal second-trimester ultrasound, which led to the detection of 49 additional cases of SCHD over 8 years. FE referrals for risk factors increased sensitivity by 3.1 percentage points (95% CI, 2.3–4.0; P<0.0001 for noninferiority).Conclusions:In the setting of a normal second-trimester ultrasound, adding a FE for risk factors offered low incremental value to the detection rate of SCHD in singleton pregnancies. The current ratio of clinical gains versus the FE resources needed to screen for SCHD in singleton pregnancies with isolated risk factors does not seem favorable. Further studies should evaluate whether these resources could be better allocated to increase SCHD sensitivity at the ultrasound level, and to help decrease heterogeneity between regions, institutions and operators.

Linking perceived to physical contrast: Comparing results from discrimination and difference-scaling experiments.

Psychophysical approaches that allow us to estimate how perceived stimulus intensity is linked to physical intensity are import tools for studying nonlinear transformations of visual signals within different visual pathways. Here, we investigated how stimulus contrast is encoded in achromatic and chromatic pathways using simple grating stimuli. We compared two experimental approaches to this question: contrast discrimination (increment detection thresholds measured on contrast pedestals) and the maximum likelihood difference scaling (MLDS) approach introduced by Maloney and Yang (2003). The results of both experiments are expressed using simple models that include a transducer function mapping physical contrast to an internal signal the observer uses in making judgments, and an estimate of the variability of this representation (internal “noise”). We found that the transducers derived from both experiments have a similar form, but occupy different ranges of physical contrast in different stimulus conditions, reflecting difference in contrast sensitivity. This is consistent with past discrimination results, and in the difference-scaling case provides new evidence supporting the idea that suprathreshold chromatic and achromatic contrast are processed similarly, once differences in contrast sensitivity are taken into account. Model estimates of internal noise were higher in the difference-scaling experiment than the discrimination experiment, a finding we attribute to a difference in task complexity. Finally, we fit an alternative version of the MLDS model in which internal noise increased with response level. This alternative was no better at predicting holdout data in a cross-validation analysis than the original constant-variance model.

Molecular and Serological Characterization of Hepatitis B Virus (HBV)-Positive Samples with Very Low or Undetectable Levels of HBV Surface Antigen.

Background: Gaps remain in the detection of nucleic acid test (NAT) yield and occult hepatitis B virus (HBV) infection (OBI) by current HBV surface antigen (HBsAg) assays. The lack of detection may be due to HBsAg levels below current assay detection limits, mutations affecting HBsAg assays or HBsAg levels, or the masking of HBsAg by antibody to HBsAg (anti-HBs). In this study, we evaluate the incremental detection of NAT yield and OBI from five diverse geographic areas by an improved sensitivity HBsAg assay and characterize the samples relative to the viral load, anti-HBs status, and PreS1–S2–S mutations. Included is a comparison population with HBV DNA levels comparable to OBI, but with readily detectable HBsAg (High Surface–Low DNA, HSLD). Methods: A total of 347 samples collected from the USA, South Africa, Spain, Cameroon, Vietnam, and Cote D’Ivoire representing NAT yield (HBsAg(−), antibody to HBV core antigen (anti-HBc)(−), HBV DNA(+), N = 131), OBI (HBsAg(−), anti-HBc(+), HBV DNA(+), N = 188), and HSLD (HBsAg(+), anti-HBc(+), HBV DNA(+), N = 28) were tested with ARCHITECT HBsAg NEXT (HBsAgNx) (sensitivity 0.005 IU/mL). The sequencing of the PreS1–S2–S genes from a subset of 177 samples was performed to determine the genotype and assess amino acid variability, particularly in anti-HBs(+) samples. Results: HBsAgNx detected 44/131 (33.6%) NAT yield and 42/188 (22.3%) OBI samples. Mean HBV DNA levels for NAT yield and OBI samples were lower in HBsAgNx(−) (50.3 and 25.9 IU/mL) than in HBsAgNx(+) samples (384.1 and 139.5 IU/mL). Anti-HBs ≥ 10 mIU/mL was present in 28.6% HBsAgNx(+) and 45.2% HBsAgNx(−) OBI, and in 3.6% HSLD samples. The genotypes were A1, A2, B, C, D, E, F, and H. There was no significant difference between HBsAgNx(−) and HBsAgNx(+) in the proportion of samples harboring substitutions or in the mean number of substitutions per sample in PreS1, PreS2, or S for the NAT yield or OBI (p range: 0.1231 to >0.9999). A total of 21/27 (77.8%) of HBsAgNx(+) OBI carried S escape mutations, insertions, or stop codons. HSLD had more PreS1 and fewer S substitutions compared to both HBsAgNx(−) and HBsAgNx(+) OBI. Mutations/deletions associated with impaired HBsAg secretion were observed in the OBI group. Conclusions: HBsAgNx provides the improved detection of NAT yield and OBI samples. Samples that remain undetected by HBsAgNx have exceptionally low HBsAg levels below the assay detection limit, likely due to low viremia or the suppression of HBsAg expression by host and viral factors.

Visual contrast perception in visual snow syndrome reveals abnormal neural gain but not neural noise.

Visual snow syndrome is a neurological condition characterized by a persistent visual disturbance, visual snow, in conjunction with additional visual symptoms. Cortical hyperexcitability is a potential pathophysiological mechanism, which could be explained by increased gain in neural responses to visual input. Alternatively, neural noise in the visual pathway could be abnormally elevated. We assessed these two potential competing neural mechanisms in our studies of visual contrast perception. Cortical hyperexcitation also occurs in migraine, which commonly co-occurs with visual snow syndrome. Therefore, to determine whether the effect of visual snow syndrome can be distinguished from interictal migraine, we recruited four participant groups: controls, migraine alone, visual snow syndrome alone and visual snow syndrome with migraine. In the first experiment, we estimated internal noise in 20 controls, 21 migraine participants and 32 visual snow syndrome participants (16 with migraine) using a luminance increment detection task. In the second experiment, we estimated neural contrast gain in 21 controls, 22 migraine participants and 35 visual snow syndrome participants (16 with migraine) using tasks assessing sensitivity to changes in contrast from a reference. Contrast gain and sensitivity were measured for the putative parvocellular and 'on' and 'off' magnocellular pathways, respectively. We found that luminance increment thresholds and internal noise estimates were normal in both visual snow syndrome and migraine. Contrast gain measures for putative parvocellular processing and contrast sensitivity for putative off magnocellular processing were abnormally increased in visual snow syndrome, regardless of migraine status. Therefore, our results indicate that visual snow syndrome is characterized by increased neural contrast gain but not abnormal neural noise within the targeted pathways.