Using Deep Learning in computer-aided diagnosis systems has been of great interest due to its impressive performance in the general domain and medical domain. However, a notable challenge is the lack of explainability of many advanced models, which poses risks in critical applications such as diagnosing findings in CXR. To address this problem, we propose ItpCtrl-AI, a novel end-to-end interpretable and controllable framework that mirrors the decision-making process of the radiologist.
View Article and Find Full Text PDFElectroencephalography (EEG) based Brain Computer Interface (BCI) systems play a significant role in facilitating how individuals with neurological impairments effectively interact with their environment. In real world applications of BCI system for clinical assistance and rehabilitation training, the EEG classifier often needs to learn on sequentially arriving subjects in an online manner. As patterns of EEG signals can be significantly different for different subjects, the EEG classifier can easily erase knowledge of learnt subjects after learning on later ones as it performs decoding in online streaming scenario, namely catastrophic forgetting.
View Article and Find Full Text PDFIEEE Winter Conf Appl Comput Vis
January 2023
Automated cellular instance segmentation is a process utilized for accelerating biological research for the past two decades, and recent advancements have produced higher quality results with less effort from the biologist. Most current endeavors focus on completely cutting the researcher out of the picture by generating highly generalized models. However, these models invariably fail when faced with novel data, distributed differently than the ones used for training.
View Article and Find Full Text PDFIntroduction: Shock index (SI) and delta shock index (∆SI) predict mortality and blood transfusion in trauma patients. This study aimed to evaluate the predictive ability of SI and ∆SI in a rural environment with prolonged transport times and transfers from critical access hospitals or level IV trauma centers.
Methods: We completed a retrospective database review at an American College of Surgeons verified level 1 trauma center for 2 years.
Background: The United States, and especially West Virginia, have a tremendous burden of coronary artery disease (CAD). Undiagnosed familial hypercholesterolemia (FH) is an important factor for CAD in the U.S.
View Article and Find Full Text PDFModern machine learning techniques (such as deep learning) offer immense opportunities in the field of human biological aging research. Aging is a complex process, experienced by all living organisms. While traditional machine learning and data mining approaches are still popular in aging research, they typically need feature engineering or feature extraction for robust performance.
View Article and Find Full Text PDFWe present a virtual reality (VR) framework for the analysis of whole human body surface area. Usual methods for determining the whole body surface area (WBSA) are based on well known formulae, characterized by large errors when the subject is obese, or belongs to certain subgroups. For these situations, we believe that a computer vision approach can overcome these problems and provide a better estimate of this important body indicator.
View Article and Find Full Text PDFThe high-frequency region of vowel signals (above the third formant or F3) has received little research attention. Recent evidence, however, has documented the perceptual utility of high-frequency information in the speech signal above the traditional frequency bandwidth known to contain important cues for speech and speaker recognition. The purpose of this study was to determine if high-pass filtered vowels could be separated by vowel category and speaker type in a supervised learning framework.
View Article and Find Full Text PDFConnectomics-the study of how neurons wire together in the brain-is at the forefront of modern neuroscience research. However, many connectomics studies are limited by the time and precision needed to correctly segment large volumes of electron microscopy (EM) image data. We present here a semi-automated segmentation pipeline using freely available software that can significantly decrease segmentation time for extracting both nuclei and cell bodies from EM image volumes.
View Article and Find Full Text PDFBackground: Gait analysis for therapy regimen prescription and monitoring requires patients to physically access clinics with specialized equipment. The timely availability of such infrastructure at the right frequency is especially important for small children. Besides being very costly, this is a challenge for many children living in rural areas.
View Article and Find Full Text PDFWe propose a model of the joint variation of shape and appearance of portions of an image sequence. The model is conditionally linear, and can be thought of as an extension of active appearance models to exploit the temporal correlation of adjacent image frames. Inference of the model parameters can be performed efficiently using established numerical optimization techniques borrowed from finite-element analysis and system identification techniques.
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