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Multi-View Information Fusion Using Multi-View Variational Autoencoder To Predict Proximal Femoral Fracture Load, Chen Zhao, Joyce H. Keyak, Xuewei Cao, Qiuying Sha, Li Wu, Zhe Luo, Lan Juan Zhao, Qing Tian, Michael Serou, Chuan Qiu, Kuan Jui Su, Hui Shen, Hong Wen Deng, Weihua Zhou
Multi-View Information Fusion Using Multi-View Variational Autoencoder To Predict Proximal Femoral Fracture Load, Chen Zhao, Joyce H. Keyak, Xuewei Cao, Qiuying Sha, Li Wu, Zhe Luo, Lan Juan Zhao, Qing Tian, Michael Serou, Chuan Qiu, Kuan Jui Su, Hui Shen, Hong Wen Deng, Weihua Zhou
Michigan Tech Publications, Part 2
Background: Hip fracture occurs when an applied force exceeds the force that the proximal femur can support (the fracture load or “strength”) and can have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions can be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, the radiation and availability of QCT limit its clinical usability. Alternative low-dose and widely available measurements, such as dual energy X-ray absorptiometry (DXA) and genetic factors, would be preferable for bone strength assessment. The aim of this paper is to design a deep learning-based model to …
Experimental Study: Deep Learning-Based Fall Monitoring Among Older Adults With Skin-Wearable Electronics, Yongkuk Lee, Suresh Pokharel, Asra Al Muslim, Dukka Kc, Kyoung Hag Lee, Woon Hong Yeo
Experimental Study: Deep Learning-Based Fall Monitoring Among Older Adults With Skin-Wearable Electronics, Yongkuk Lee, Suresh Pokharel, Asra Al Muslim, Dukka Kc, Kyoung Hag Lee, Woon Hong Yeo
Michigan Tech Publications
Older adults are more vulnerable to falling due to normal changes due to aging, and their falls are a serious medical risk with high healthcare and societal costs. However, there is a lack of automatic fall detection systems for older adults. This paper reports (1) a wireless, flexible, skin-wearable electronic device for both accurate motion sensing and user comfort, and (2) a deep learning-based classification algorithm for reliable fall detection of older adults. The cost-effective skin-wearable motion monitoring device is designed and fabricated using thin copper films. It includes a six-axis motion sensor and is directly laminated on the skin …
Plmsnosite: An Ensemble-Based Approach For Predicting Protein S-Nitrosylation Sites By Integrating Supervised Word Embedding And Embedding From Pre-Trained Protein Language Model, Pawel Pratyush, Suresh Pokharel, Hiroto Saigo, Dukka Kc
Plmsnosite: An Ensemble-Based Approach For Predicting Protein S-Nitrosylation Sites By Integrating Supervised Word Embedding And Embedding From Pre-Trained Protein Language Model, Pawel Pratyush, Suresh Pokharel, Hiroto Saigo, Dukka Kc
Michigan Tech Publications
Background: Protein S-nitrosylation (SNO) plays a key role in transferring nitric oxide-mediated signals in both animals and plants and has emerged as an important mechanism for regulating protein functions and cell signaling of all main classes of protein. It is involved in several biological processes including immune response, protein stability, transcription regulation, post translational regulation, DNA damage repair, redox regulation, and is an emerging paradigm of redox signaling for protection against oxidative stress. The development of robust computational tools to predict protein SNO sites would contribute to further interpretation of the pathological and physiological mechanisms of SNO. Results: Using an …
A Non-Reference Evaluation Of Underwater Image Enhancement Methods Using A New Underwater Image Dataset, Ashraf Saleem, Sidike Paheding, Nathir Rawashdeh, Ali Awad, Navjot Kaur
A Non-Reference Evaluation Of Underwater Image Enhancement Methods Using A New Underwater Image Dataset, Ashraf Saleem, Sidike Paheding, Nathir Rawashdeh, Ali Awad, Navjot Kaur
Michigan Tech Publications
The rise of vision-based environmental, marine, and oceanic exploration research highlights the need for supporting underwater image enhancement techniques to help mitigate water effects on images such as blurriness, low color contrast, and poor quality. This paper presents an evaluation of common underwater image enhancement techniques using a new underwater image dataset. The collected dataset is comprised of 100 images of aquatic plants taken at a shallow depth of up to three meters from three different locations in the Great Lake Superior, USA, via a Remotely Operated Vehicle (ROV) equipped with a high-definition RGB camera. In particular, we use our …
Deep Learning For Medical Image Segmentation Using Prior Knowledge And Topology, Chen Zhao
Deep Learning For Medical Image Segmentation Using Prior Knowledge And Topology, Chen Zhao
Dissertations, Master's Theses and Master's Reports
Image segmentation refers to the division of a digital image into distinct segments or groups of pixels/voxels. However, most of the existing deep learning approaches lack the utilization of prior knowledge, such as shape information, which could improve segmentation accuracy. In addition, conventional image segmentation frequently falls short in preserving intricate spatial details, motivating the innovation of strategies for multi-scaled feature integration. Furthermore, traditional image segmentation methods primarily concentrate on pixel-level or region-level analysis. However, given the inherent morphological similarities among various image objects, the significance of topology information surpasses that of pixel-level data in the realm of medical image …
An Autoencoder-Based Deep Learning Method For Genotype Imputation, Meng Song, Jonathan Greenbaum, Joseph Luttrell, Weihua Zhou, Chong Wu, Zhe Luo, Chuan Qiu, Lan Juan Zhao, Kuan Jui Su, Qing Tian, Hui Shen, Huixiao Hong, Ping Gong, Xinghua Shi, Hong Wen Deng, Chaoyang Zhang
An Autoencoder-Based Deep Learning Method For Genotype Imputation, Meng Song, Jonathan Greenbaum, Joseph Luttrell, Weihua Zhou, Chong Wu, Zhe Luo, Chuan Qiu, Lan Juan Zhao, Kuan Jui Su, Qing Tian, Hui Shen, Huixiao Hong, Ping Gong, Xinghua Shi, Hong Wen Deng, Chaoyang Zhang
Michigan Tech Publications
Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a …
Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang
Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang
Michigan Tech Publications
The Laurentian Great Lakes, one of the world’s largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, …
Jamming Detection And Classification In Ofdm-Based Uavs Via Feature- And Spectrogram-Tailored Machine Learning, Y. Li, J. Pawlak, J. Price, K. Al Shamaileh, Q. Niyaz, S. Paheding, V. Devabhaktuni
Jamming Detection And Classification In Ofdm-Based Uavs Via Feature- And Spectrogram-Tailored Machine Learning, Y. Li, J. Pawlak, J. Price, K. Al Shamaileh, Q. Niyaz, S. Paheding, V. Devabhaktuni
Michigan Tech Publications
In this paper, a machine learning (ML) approach is proposed to detect and classify jamming attacks against orthogonal frequency division multiplexing (OFDM) receivers with applications to unmanned aerial vehicles (UAVs). Using software-defined radio (SDR), four types of jamming attacks; namely, barrage, protocol-aware, single-tone, and successive-pulse are launched and investigated. Each type is qualitatively evaluated considering jamming range, launch complexity, and attack severity. Then, a systematic testing procedure is established by placing an SDR in the vicinity of a UAV (i.e., drone) to extract radiometric features before and after a jamming attack is launched. Numeric features that include signal-to-noise ratio (SNR), …
Synthetic Augmentation Methods For Object Detection In Overhead Imagery, Nicholas R. Hamilton
Synthetic Augmentation Methods For Object Detection In Overhead Imagery, Nicholas R. Hamilton
Dissertations, Master's Theses and Master's Reports
The multidisciplinary area of geospatial intelligence (GEOINT) is continually changing and becoming more complex. From efforts to automate portions of GEOINT using machine learning, which augment the analyst and improve exploitation, to optimizing the growing number of sources and variables, there is no denying that the strategies involved in this collection method are rapidly progressing. The unique and inherent complexities involved in imagery analysis from an overhead perspective--—e.g., target resolution, imaging band(s), and imaging angle--—test the ability of even the most developed and novel machine learning techniques. To support advancement in the application of object detection in overhead imagery, we …
Toward Deep Learning Emulators For Modeling The Large-Scale Structure Of The Universe, Neerav Kaushal
Toward Deep Learning Emulators For Modeling The Large-Scale Structure Of The Universe, Neerav Kaushal
Dissertations, Master's Theses and Master's Reports
Multi-billion dollar cosmological surveys are being conducted almost every decade in today’s era of precision cosmology. These surveys scan vast swaths of sky and generate tons of observational data. In order to extract meaningful information from this data and test these observations against theory, rigorous theoretical predictions are needed. In the absence of an analytic method, cosmological simulations become the most widely used tool to provide these predictions in order to test against the observations. They can be used to study covariance matrices, generate mock galaxy catalogs and provide ready-to-use snapshots for detailed redshift analyses. But cosmological simulations of matter …
St-V-Net: Incorporating Shape Prior Into Convolutional Neural Networks For Proximal Femur Segmentation, Chen Zhao, Joyce H. Keyak, Jinshan Tang, Tadashi S. Kaneko, Sundeep Khosla, Weihua Zhou, Et. Al.
St-V-Net: Incorporating Shape Prior Into Convolutional Neural Networks For Proximal Femur Segmentation, Chen Zhao, Joyce H. Keyak, Jinshan Tang, Tadashi S. Kaneko, Sundeep Khosla, Weihua Zhou, Et. Al.
Michigan Tech Publications
We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the …
U-Net And Its Variants For Medical Image Segmentation: A Review Of Theory And Applications, Nahian Siddique, Paheding Sidike, Colin P. Elkin, Vijay Devabhaktuni
U-Net And Its Variants For Medical Image Segmentation: A Review Of Theory And Applications, Nahian Siddique, Paheding Sidike, Colin P. Elkin, Vijay Devabhaktuni
Michigan Tech Publications
U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines …
Accurate Diagnosis Of Colorectal Cancer Based On Histopathology Images Using Artificial Intelligence, K. S. Wang, G. Yu, C. Xu, X. H. Meng, J. Zhou, W. Zhou, Et. Al.
Accurate Diagnosis Of Colorectal Cancer Based On Histopathology Images Using Artificial Intelligence, K. S. Wang, G. Yu, C. Xu, X. H. Meng, J. Zhou, W. Zhou, Et. Al.
Michigan Tech Publications
Background: Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Methods: Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, > 14,680 WSIs, from > 9631 subjects that covered …