Role of three dimensional ultrasound in uterine anomalies - 3D assessment of cervix in septate uteri

Background: Mullerian duct anomalies are associated with higher incidence of infertility, recurrent pregnancy loss and fetal complications. The role of imaging is to distinguish between surgically correctable forms of mullerian duct anomalies from the inoperable forms. HSG and 2D ultrasound does not give a definitive diagnosis in double uterine cavity anomalies and moreover septate uterus with double ostia can be misinterpreted as unicornuate uterus on HSG as the dye passes through only one ostium. The study highlights the usage of transvaginal 3D ultrasound in assessment of the cervix in septate uterus.Methods: Volume acquisition was obtained in the sagittal section of uterus by transvaginal route. Under adequate magnification care was taken to include the cervix and then the volume was acquired. The rendering box was adjusted and the green line placed in the region of cervix to get the satisfactory transverse rendered image of cervix.Results: 3D assessment of cervix in 44 septate uterus revealed single cervical ostium in 36, complete septum with double ostia in 8 cases of which one had a duplicated cervix. There was absolute correlation between 3D assessment of the cervix and clinical evaluation.Conclusions: 3D transvaginal ultrasonography of the uterine cavity is extremely accurate in diagnosing and classifying anomalies. Assessment of double uterine cavity abnormalities is complete with 3D evaluation of cervix. The added advantage of assessing cervix with 3D helps to distinguish between single and double ostia in septate cervix

Using Functional Near Infrared Spectroscopy to Assess Cognitive Workload

Quantification of mental workload is a significant aspect of monitoring and adaptive aiding systems that are intended to improve the efficiency and safety of human–machine systems. Functional near Infrared (fNIR) spectroscopy is a field-deployable brain monitoring device that provides a measures of cerebral hemodynamic within the prefrontal cortex. The purpose of this study was to assess the cognitive load by using Performance (reaction time), Behavioral metrics (NASA TLX) and Neuro-Cognitive Measures (Hemodynamic response). To observe the activation in prefrontal cortex, we employed Functional Near Infrared (fNIR) Spectroscopy with a Standard Stroop task. A total of 25 healthy participants (N 18 Male and N 07 Female, M Age 25.5 SD 7.6), participated in the study. For statistical analysis, a repeated measure t-test was computed to compare the Oxy (Δ[HbO2]) and De-Oxy (Δ[hHb]) changes under Congruent and In-Congruent task conditions. For Classification, Binary logistic regression model applied to identify how accurately classifying the varied workload conditions. The finding shows that fNIR measures had adequate predictive power for estimating task performance in workload conditions. In this paper, we have found evidence that fNIR can be used as indicator of cognitive load which is important for optimal human performance

Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks

With the rise of deep neural networks, the challenge of explaining the predictions of these networks has become increasingly recognized. While many methods for explaining the decisions of deep neural networks exist, there is currently no consensus on how to evaluate them. On the other hand, robustness is a popular topic for deep learning research; however, it is hardly talked about in explainability until very recently. In this tutorial paper, we start by presenting gradient-based interpretability methods. These techniques use gradient signals to assign the burden of the decision on the input features. Later, we discuss how gradient-based methods can be evaluated for their robustness and the role that adversarial robustness plays in having meaningful explanations. We also discuss the limitations of gradient-based methods. Finally, we present the best practices and attributes that should be examined before choosing an explainability method. We conclude with the future directions for research in the area at the convergence of robustness and explainability

Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks

With the rise of deep neural networks, the challenge of explaining the predictions of these networks has become increasingly recognized. While many methods for explaining the decisions of deep neural networks exist, there is currently no consensus on how to evaluate them. On the other hand, robustness is a popular topic for deep learning research; however, it is hardly talked about in explainability until very recently. In this tutorial paper, we start by presenting gradient-based interpretability methods. These techniques use gradient signals to assign the burden of the decision on the input features. Later, we discuss how gradient-based methods can be evaluated for their robustness and the role that adversarial robustness plays in having meaningful explanations. We also discuss the limitations of gradient-based methods. Finally, we present the best practices and attributes that should be examined before choosing an explainability method. We conclude with the future directions for research in the area at the convergence of robustness and explainability.Comment: 23 pages, 4 figure

Targeted Background Removal Creates Interpretable Feature Visualizations

Feature visualization is used to visualize learned features for black box machine learning models. Our approach explores an altered training process to improve interpretability of the visualizations. We argue that by using background removal techniques as a form of robust training, a network is forced to learn more human recognizable features, namely, by focusing on the main object of interest without any distractions from the background. Four different training methods were used to verify this hypothesis. The first used unmodified pictures. The second used a black background. The third utilized Gaussian noise as the background. The fourth approach employed a mix of background removed images and unmodified images. The feature visualization results show that the background removed images reveal a significant improvement over the baseline model. These new results displayed easily recognizable features from their respective classes, unlike the model trained on unmodified data

Pore resistivity variation by Resistivity imaging technique in sedimentary part of main Gadilam river basin, Cuddalore District, Tamil Nadu, India

Electrical resistivity is the only property of physics which give information of subsurface moisture content in the formation, Hence geophysical electrical resistivity survey was carried out to investigate the nature of shallow subsurface formations and geological contact in the main Gadilam river basin of Cuddalore District in Tamil Nadu. Twenty-seven vertical electrical soundings (VES) were conducted by Schlumberger configuration in the basin. Data is interpreted by curve matching techniques using IPI2 WIN software, layer parameters like apparent resistivity (?a) and thickness (h) interpretation were exported to Geographic Information System (GIS). Interpretation distinguishes three major geoelectric layers like topsoil, sandy clay layer, clayey sand layer along the contact zone in the basin. Interpreted VES sounding curves are mostly four-layer cases of QH, H, HA and KH type. Investigation demarcates lithology of subsurface and hydrogeological set up by employing maximum possible electrode sounding to infer saline water and freshwater occurrence based on resistivity signals. Zone of groundwater potential map was prepared with the combination of resistivity (?= ?1+ ?2+ ?3+ ?4) and corresponding thickness (T= T1+T2+T3+T4). High resistivity value of >200 ? m and low resistivity value of <10 ? m show the occurrence of alkaline and saline water within the formation aquifers as a result of possible rock water interaction and saline water dissolution. Four-layer resistivity cases from the matched curve (namely KH, AH, QA, and KA type) show the resistivity distribution/variation. It separates the freshwater depth wish from 1 to 140 ? m in fluvial sediments. Flood basin, sandstone and clay layer with low resistivity value of 3.16 - 7.5 ? m indicates contact with saline and freshwater aquifer. The Iso – resistivity map delineates saline water and freshwater zones with in the fourth layer cases in the same locations to indicate the irrational way of abstracting groundwater, resulting in saltwater ingress

Deployment of a Robust and Explainable Mortality Prediction Model: The COVID-19 Pandemic and Beyond

This study investigated the performance, explainability, and robustness of deployed artificial intelligence (AI) models in predicting mortality during the COVID-19 pandemic and beyond. The first study of its kind, we found that Bayesian Neural Networks (BNNs) and intelligent training techniques allowed our models to maintain performance amidst significant data shifts. Our results emphasize the importance of developing robust AI models capable of matching or surpassing clinician predictions, even under challenging conditions. Our exploration of model explainability revealed that stochastic models generate more diverse and personalized explanations thereby highlighting the need for AI models that provide detailed and individualized insights in real-world clinical settings. Furthermore, we underscored the importance of quantifying uncertainty in AI models which enables clinicians to make better-informed decisions based on reliable predictions. Our study advocates for prioritizing implementation science in AI research for healthcare and ensuring that AI solutions are practical, beneficial, and sustainable in real-world clinical environments. By addressing unique challenges and complexities in healthcare settings, researchers can develop AI models that effectively improve clinical practice and patient outcomes

Board # 29 : A PATTERN RECOGNITION APPROACH TO SIGNAL TO NOISE RATIO ESTIMATION OF SPEECH

A blind approach for estimating the signal to noise ratio (SNR) of a speech signal corrupted by additive noise is proposed. The method is based on a pattern recognition paradigm using various linear predictive based features, a vector quantizer classifier and estimation combination. Blind SNR estimation is very useful in biometric speaker identification systems in which a confidence metric is determined along with the speaker identity. The confidence metric is partially based on the mismatch between the training and testing conditions of the speaker identification system and SNR estimation is very important in evaluating the degree of this mismatch. The educational impact of this project is two-fold: 1. Undergraduate students are initiated into research/development by working on a team to achieve a software implementation of the SNR estimation system. The students will also evaluate the performance of the system by experimenting with different features and classifiers. Producing a paper in a refereed technical conference is the objective. 2. The students will also write a laboratory manual for a portion of this project to be run in a junior level signals and systems class and a senior level class on speech processing. Producing a paper in a refereed education conference is the objective. The learning outcomes for the students engaged in research and for the students doing the project in a class include: • Enhanced application of math skills • Enhanced software implementation skills • Enhanced interest in signal processing • Enhanced ability to analyze experimental results • Enhanced communication skills. The assessment instruments include: • Student surveys (target versus control group comparison that includes a statistical analysis) • Faculty tracking of student learning outcomes based on student work • Faculty evaluation of student work based on significant rubrics • A concept inventory tes

Active Analog Circuit Design: Laboratory Project and Assessment

It is very important that undergraduate teaching of analog circuits be rigorous, involve a laboratory component and stimulate student interest. This paper describes a three week module on active circuits that incorporates circuit design, analysis and testing. The lectures are integrated with the laboratory component and all appropriate concepts in mathematics are covered. Assessment results are based on running the project at three universities, namely, Rowan, Bucknell and Tennessee State. Quantitative results based on student surveys, a concept inventory test and faculty formulated rubrics demonstrate the accomplishment of the learning outcomes

Configuration and Assessment of a Senior Level Course in Biometric Systems

It is very important that modern topics be covered at the senior undergraduate level in order that students benefit from (1) advanced STEM concepts, (2) project based learning, (3) a systems level perspective and (4) real world applications. This will help students that proceed to graduate school and who take up employment in government or industry. This paper describes a senior level undergraduate course in biometrics, a multidisciplinary area that is highly relevant to society and which has a rapidly growing global market. The course objectives, broad learning outcomes and curricular plan are described. Assessment results based on the analysis of a concept inventory test and student surveys (target versus control group) related to the learning outcomes show that the course was very successful