Browsing by Author "Do, Nhan"

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    AI in Medical Imaging Informatics: Current Challenges and Future Directions
    (IEEE, 2020-07) Panayides, Andreas S.; Amini, Amir; Filipovic, Nenad D.; Sharma, Ashish; Tsaftaris, Sotirios A.; Young, Alistair; Foran, David; Do, Nhan; Golemati, Spyretta; Kurc, Tahsin; Huang, Kun; Nikita, Konstantina S.; Veasey, Ben P.; Zervakis, Michalis; Saltz, Joel H.; Pattichis, Constantinos S.; Biostatistics & Health Data Science, School of Medicine
    This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.
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    A Case Study for Massive Text Mining: K Nearest Neighbor Algorithm on PubMed data
    (Office of the Vice Chancellor for Research, 2015-04-17) Do, Nhan; Dundar, Murat
    US National Library of Medicine (NLM) has a huge collections of millions of books, journals, and other publications relating to medical domain. NLM creates the database called MEDLINE to store and link the citations to the publications. This database allows the researchers and students to access and find medical articles easily. The public can search on MEDLINE using a database called PubMed. When the new PubMed documents become available online, the curators have to manually decide the labels for them. The process is tedious and time-consuming because there are more than 27,149 descriptor (MeSH terms). Although the curators are already using a system called MTI for MeSH terms suggestion, the performance needs to be improved. This research explores the usage of text classification to annotate new PubMed document automatically, efficiently, and with reasonable accuracy. The data is gathered from BioASQ Contest, which contains 4 millions of abstracts. The research process includes preprocess the data, reduce the feature space, classify and evaluate the result. We focus on the K nearest neighbor algorithm in this case study.
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    Multi-reference global registration of individual A-lines in adaptive optics optical coherence tomography retinal images
    (Elsevier, 2021) Kurokawa, Kazuhiro; Crowell, James A.; Do, Nhan; Lee, John J.; Miller, Donald T.; Engineering Technology, Purdue School of Engineering and Technology
    Significance: Adaptive optics optical coherence tomography (AO-OCT) technology enables non-invasive, high-resolution three-dimensional (3D) imaging of the retina and promises earlier detection of ocular disease. However, AO-OCT data are corrupted by eye-movement artifacts that must be removed in post-processing, a process rendered time-consuming by the immense quantity of data. Aim: To efficiently remove eye-movement artifacts at the level of individual A-lines, including those present in any individual reference volume. Approach: We developed a registration method that cascades (1) a 3D B-scan registration algorithm with (2) a global A-line registration algorithm for correcting torsional eye movements and image scaling and generating global motion-free coordinates. The first algorithm corrects 3D translational eye movements to a single reference volume, accelerated using parallel computing. The second algorithm combines outputs of multiple runs of the first algorithm using different reference volumes followed by an affine transformation, permitting registration of all images to a global coordinate system at the level of individual A-lines. Results: The 3D B-scan algorithm estimates and corrects 3D translational motions with high registration accuracy and robustness, even for volumes containing microsaccades. Averaging registered volumes improves our image quality metrics up to 22 dB. Implementation in CUDA™ on a graphics processing unit registers a 512 × 512 × 512 volume in only 10.6 s, 150 times faster than MATLAB™ on a central processing unit. The global A-line algorithm minimizes image distortion, improves regularity of the cone photoreceptor mosaic, and supports enhanced visualization of low-contrast retinal cellular features. Averaging registered volumes improves our image quality up to 9.4 dB. It also permits extending the imaging field of view (∼2.1 × ) and depth of focus (∼5.6 × ) beyond what is attainable with single-reference registration. Conclusions: We can efficiently correct eye motion in all 3D at the level of individual A-lines using a global coordinate system.
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    Towards Training the Extended Voltage Manifold Computer (EVMC) using Particle Swarm Optimization
    (Office of the Vice Chancellor for Research, 2014-04-11) Bertram, Michael J; Do, Nhan; Gramlin, Lucas; Yoshida, Ken; Salama, Paul; Himebaugh, Bryce
    Extended Analog Computers (EAC) have been explored as a substrate for unconventional computing techniques since the early 1990s. A particular strength of the technique is the near instantaneous speed it solves computational problems. However, application of the EAC and specific EAC classes, as the Extended Voltage Manifold Computer (EVMC), to real-world problems await the development of methods to program EACs. A property of the EVMC is that each output voltage can be described by a class of radial basis functions (RBF). Linking multiple EVMCs, a neural network called a radial basis function network (RBFN) can be implemented. The specific aim of this work is to develop the means to train EVMCs and networks of EVMC based RBFNs. The strategy employed in the present work is to develop a method using EVMCs implemented as finite element method (FEM) simulations to define the error state-space and error gradient of the untrained EVMC manifold. Once defined the EVMC simulation can be recursively configured to reduce the error in a Hebbian sense. Furthermore, particle swarm optimization (PSO) is being explored to improve the speed of convergence. FEM simulations were constructed using COMSOL Multiphysics to model EVMC manifolds in different states. In parallel, a particle swarm optimizer was altered to demonstrate training of simple RBF manifolds. Examination of FEM simulations verified the kernel function as hyperbolic and radially based. These preliminary findings indicated that the EVMC can be accurately modeled and manipulated using COMSOL, and PSO can be used once the error manifold is defined. From this we can take the possibility of improving the speed of training the EVMC via PSO. The next step to verify this possibility is to combine the COMSOL and Python codes to confirm the EVMC can be trained.