Feedback-assisted automatic target and clutter discrimination using a Bayesian convolutional neural network for improved explainability in SAR applications

DATA AVAILABILITY STATEMENT : The NATO-SET 250 dataset is not publicly available; however, the MSTAR dataset can be found at the following url: https://www.sdms.afrl.af.mil/index.php?collection=mstar (accessed on 5 January 2022).In this paper, a feedback training approach for efficiently dealing with distribution shift in synthetic aperture radar target detection using a Bayesian convolutional neural network is proposed. After training the network on in-distribution data, it is tested on out-of-distribution data. Samples that are classified incorrectly with high certainty are fed back for a second round of training. This results in the reduction of false positives in the out-of-distribution dataset. False positive target detections challenge human attention, sensor resource management, and mission engagement. In these types of applications, a reduction in false positives thus often takes precedence over target detection and classification performance. The classifier is used to discriminate the targets from the clutter and to classify the target type in a single step as opposed to the traditional approach of having a sequential chain of functions for target detection and localisation before the machine learning algorithm. Another aspect of automated synthetic aperture radar detection and recognition problems addressed here is the fact that human users of the output of traditional classification systems are presented with decisions made by “black box” algorithms. Consequently, the decisions are not explainable, even to an expert in the sensor domain. This paper makes use of the concept of explainable artificial intelligence via uncertainty heat maps that are overlaid onto synthetic aperture radar imagery to furnish the user with additional information about classification decisions. These uncertainty heat maps facilitate trust in the machine learning algorithm and are derived from the uncertainty estimates of the classifications from the Bayesian convolutional neural network. These uncertainty overlays further enhance the users’ ability to interpret the reasons why certain decisions were made by the algorithm. Further, it is demonstrated that feeding back the high-certainty, incorrectly classified out-of-distribution data results in an average improvement in detection performance and a reduction in uncertainty for all synthetic aperture radar images processed. Compared to the baseline method, an improvement in recall of 11.8%, and a reduction in the false positive rate of 7.08% were demonstrated using the Feedback-assisted Bayesian Convolutional Neural Network or FaBCNN.The Radar and Electronic Warfare department at the CSIR.http://www.mdpi.com/journal/remotesensinghj2023Electrical, Electronic and Computer Engineerin

Calcifying Odontogenic Cysts: A 20-year Retrospective Clinical and Radiological Review

https://drive.google.com/file/d/1ITFvU7cm3hNQyBRHV6LwRI47pgIWN37E/view?usp=sharinghttps://drive.google.com/drive/folders/1Rm_GimQtumx7roigsBwBPMnhFTTUZ-UC?usp=sharinghttps://drive.google.com/drive/folders/1TtTOHdcYPzNddB4h2MCDaXeYTkkHn_v7?usp=sharin

How should the completeness and quality of curated nanomaterial data be evaluated?

Nanotechnology is of increasing significance. Curation of nanomaterial data into electronic databases offers opportunities to better understand and predict nanomaterials' behaviour. This supports innovation in, and regulation of, nanotechnology. It is commonly understood that curated data need to be sufficiently complete and of sufficient quality to serve their intended purpose. However, assessing data completeness and quality is non-trivial in general and is arguably especially difficult in the nanoscience area, given its highly multidisciplinary nature. The current article, part of the Nanomaterial Data Curation Initiative series, addresses how to assess the completeness and quality of (curated) nanomaterial data. In order to address this key challenge, a variety of related issues are discussed: the meaning and importance of data completeness and quality, existing approaches to their assessment and the key challenges associated with evaluating the completeness and quality of curated nanomaterial data. Considerations which are specific to the nanoscience area and lessons which can be learned from other relevant scientific disciplines are considered. Hence, the scope of this discussion ranges from physicochemical characterisation requirements for nanomaterials and interference of nanomaterials with nanotoxicology assays to broader issues such as minimum information checklists, toxicology data quality schemes and computational approaches that facilitate evaluation of the completeness and quality of (curated) data. This discussion is informed by a literature review and a survey of key nanomaterial data curation stakeholders. Finally, drawing upon this discussion, recommendations are presented concerning the central question: how should the completeness and quality of curated nanomaterial data be evaluated

Passive ISAR using target-borne illuminators of opportunity

This paper proposes a new idea for forming passive ISAR images when the illuminator of opportunity is on the target to be imaged. A theoretical approach is proposed that demonstrate its feasibility in the 2D domain. Practical issues and limitations are also discussed and some simulation results are shown to support the theory

Psychiatric comorbidity in treatment-seeking alcoholics: The role of childhood trauma and perceived parental dysfunction

Background: This study among treatment-seeking alcoholics examined the relationship between childhood abuse (sexual Abuse only [CSA], physical abuse only [CPA], or dual abuse [CDA]) and the presence of comorbid affective disorders, anxiety disorders, and suicide attempts, controlling for the potential confounding effects of other childhood adversities (early parental loss, witnessing domestic, violence, parental alcoholism, and/or dysfunction) and adult assault histories. Method: We assessed 155 (33 females, 122 males) treatment-seeking alcoholics using the European Addiction Se verity Index, the Structured Trauma Interview, and the Composite International Diagnostic Interview. Results: The severity of childhood abuse was associated with posttraumatic stress disorder (PTSD) and suicide attempts in females and with PTSD, social phobia, agoraphobia, and dysthymia in males. Among men, multiple logistic regression models showed that CPA and CDA were not independently associated with any of the examined comorbid disorders or with suicide attempts. However, CSA independently predicted comorbid social phobia, agoraphobia, and PTSD. For the presence of comorbid affective disorders (mainly major depression) and suicide attempts, maternal dysfunctioning was particularly important. CSA also independently contributed to the number of comorbid diagnoses. For females, small sample size precluded the use of multivariate analyses. Conclusion: Childhood abuse is an important factor in understanding clinical impairment in treated alcoholics, especially regarding comorbid phobic anxiety disorders, PTSD, and suicidality. These findings underline the importance of routine assessment of childhood trauma and possible trauma-related disorders in individuals presenting to alcohol treatment services. More studies with bigger samples sizes of female alcohol-dependent patients are neede

Assessment of lifetime physical and sexual abuse in treated alcoholics Validity of the Addiction Severity Index

We examined the validity of the Addiction Severity Index (ASI) regarding the identification of lifetime physical and sexual abuse histories using the Structured Trauma Interview (STI) as external criterion in alcohol-dependent patients (n = 144). Compared to the STI, the ASI showed a lower incidence of lifetime physical abuse reports (51% vs. 24%) and lifetime sexual abuse reports (29% vs. 17%). Lower incidence of abuse reports was stronger in males compared to females, which could be largely explained by ASI perpetrator restrictions (i.e. exclusion of several extrafamilial perpetrators). Controlling for these restrictions, acceptable sensitivity for both sexual and physical abuse as well as good specificity was found. Data indicated no response bias on the ASI in terms of social desirability or abuse severity. Sensitivity of the ASI method can probably be improved by including an opening preface to the subsequent abuse questions, including questions inquiring about abuse histories that have neutral wording instead of using the word "abuse," and inclusion of all possible perpetrators. (C) 2002 Elsevier Science Ltd. All rights reserve

Trauma and dissociation in treatment-seeking alcoholics: Towards a resolution of inconsistent findings

There is consistent empirical evidence for a trauma-dissociation relation in general population samples and in psychiatric patients. However, contradictory findings have been reported on this relation among substance abusers. The present study attempts to resolve these inconsistencies by testing a series of hypotheses related to problems regarding the measurement of childhood abuse, the measurement of psychological dissociation, and the potential existence of substance abuse as a form of chemical dissociation. Alcoholic patients (N = 155) were administered the Dissociative Experiences Scale (DES), the Structured Trauma Interview (STI), the European Addiction Severity Index (EuropASI), and the Posttraumatic Stress Disorder (PTSD) section of the Composite International Diagnostic Interview (CIDI). The DES showed good psychometric properties. Substantial rates of traumatization and PTSD were observed, as well as a significant trauma-PTSD relation. However, the mean DES score was low (11.4) and dissociation was not related to trauma (childhood or lifetime) or to PTSD. Years of lifetime regular medicine use, however, was significantly correlated with the severity of dissociative symptoms and PTSD, particularly in males. Overall, these findings suggest that absence of a trauma-dissociation relation in alcoholics may not be due to measurement problems of childhood abuse and/or dissociation. Rather, a trauma-dissociation link may not exist, particularly in male alcoholics, because these individuals may abuse substances to achieve dissociative-like states. Additional research is needed to further evaluate the utility of the DES in alcoholic samples and to examine the notion of chemical dissociation. Copyright 2002, Elsevier Science (USA). All rights reserve

Gender differences in posttraumatic stress disorder

One of the most consistent findings in the epidemiology of posttraumatic stress disorder (PTSD) is the higher risk of this disorder in women. Explanations reviewed within a psychobiological model of PTSD suggest that women's higher PTSD risk may be due to the type of trauma they experience, their younger age at the time of trauma exposure, their stronger perceptions of threat and loss of control, higher levels of peri-traumatic dissociation, insufficient social support resources, and greater use of alcohol to manage trauma-related symptoms like intrusive memories and dissociation, as well as gender-specific acute psychobiological reactions to trauma. This review demonstrates the need for additional research of the gender differences in posttraumatic stress. Recommendations are made for clinical practic

Feedback-Assisted Automatic Target and Clutter Discrimination Using a Bayesian Convolutional Neural Network for Improved Explainability in SAR Applications

In this paper, a feedback training approach for efficiently dealing with distribution shift in synthetic aperture radar target detection using a Bayesian convolutional neural network is proposed. After training the network on in-distribution data, it is tested on out-of-distribution data. Samples that are classified incorrectly with high certainty are fed back for a second round of training. This results in the reduction of false positives in the out-of-distribution dataset. False positive target detections challenge human attention, sensor resource management, and mission engagement. In these types of applications, a reduction in false positives thus often takes precedence over target detection and classification performance. The classifier is used to discriminate the targets from the clutter and to classify the target type in a single step as opposed to the traditional approach of having a sequential chain of functions for target detection and localisation before the machine learning algorithm. Another aspect of automated synthetic aperture radar detection and recognition problems addressed here is the fact that human users of the output of traditional classification systems are presented with decisions made by “black box” algorithms. Consequently, the decisions are not explainable, even to an expert in the sensor domain. This paper makes use of the concept of explainable artificial intelligence via uncertainty heat maps that are overlaid onto synthetic aperture radar imagery to furnish the user with additional information about classification decisions. These uncertainty heat maps facilitate trust in the machine learning algorithm and are derived from the uncertainty estimates of the classifications from the Bayesian convolutional neural network. These uncertainty overlays further enhance the users’ ability to interpret the reasons why certain decisions were made by the algorithm. Further, it is demonstrated that feeding back the high-certainty, incorrectly classified out-of-distribution data results in an average improvement in detection performance and a reduction in uncertainty for all synthetic aperture radar images processed. Compared to the baseline method, an improvement in recall of 11.8%, and a reduction in the false positive rate of 7.08% were demonstrated using the Feedback-assisted Bayesian Convolutional Neural Network or FaBCNN