Engineering Commons^™

A Deep Learning Framework For Joint Image Restoration And Recognition, Ruilong Chen, Lyudmila Mihaylova, Hao Zhu, Nidhal Carla Bouaynaya

Henry M. Rowan College of Engineering Faculty Scholarship

Image restoration and recognition are important computer vision tasks representing an inherent part of autonomous systems. These two tasks are often implemented in a sequential manner, in which the restoration process is followed by a recognition. In contrast, this paper proposes a joint framework that simultaneously performs both tasks within a shared deep neural network architecture. This joint framework integrates the restoration and recognition tasks by incorporating: (i) common layers, (ii) restoration layers and (iii) classification layers. The total loss function combines the restoration and classification losses. The proposed joint framework, based on capsules, provides an efficient solution that can …

Diagnosing Growth In Low-Grade Gliomas With And Without Longitudinal Volume Measurements: A Retrospective Observational Study., Hassan M Fathallah-Shaykh, Andrew Deatkine, Elizabeth Coffee, Elias Khayat, Asim K Bag, Xiaosi Han, Paula Province Warren, Markus Bredel, John Fiveash, James Markert, Nidhal Carla Bouaynaya, Louis B Nabors

Henry M. Rowan College of Engineering Faculty Scholarship

BACKGROUND: Low-grade gliomas cause significant neurological morbidity by brain invasion. There is no universally accepted objective technique available for detection of enlargement of low-grade gliomas in the clinical setting; subjective evaluation by clinicians using visual comparison of longitudinal radiological studies is the gold standard. The aim of this study is to determine whether a computer-assisted diagnosis (CAD) method helps physicians detect earlier growth of low-grade gliomas.

METHODS AND FINDINGS: We reviewed 165 patients diagnosed with grade 2 gliomas, seen at the University of Alabama at Birmingham clinics from 1 July 2017 to 14 May 2018. MRI scans were collected during …

An Integrated Approach For Remanufacturing Job Shop Scheduling With Routing Alternatives., Ling Ling Li, Cong Bo Li, Li Li, Ying Tang, Qing Shan Yang

Henry M. Rowan College of Engineering Faculty Scholarship

Remanufacturing is a practice of growing importance due to increasing environmental awareness and regulations. However, the stochastic natures inherent in the remanufacturing processes complicate its scheduling. This paper undertakes the challenge and presents a remanufacturing job shop scheduling approach by integrating alternative routing assignment and machine resource dispatching. A colored timed Petri net is introduced to model the dynamics of remanufacturing process, such as various process routings, uncertain operation times for cores, and machine resource conflicts. With the color attributes in Petri nets, two types of decision points, recovery routing selection and resource dispatching, are introduced and linked with places …

Approximate Kernel Reconstruction For Time-Varying Networks, Gregory Ditzler, Nidhal Carla Bouaynaya, Roman Shterenberg, Hassan Fathaliah-Shaykh

Henry M. Rowan College of Engineering Faculty Scholarship

Most existing algorithms for modeling and analyzing molecular networks assume a static or time-invariant network topology. Such view, however, does not render the temporal evolution of the underlying biological process as molecular networks are typically “re-wired” over time in response to cellular development and environmental changes. In our previous work, we formulated the inference of time-varying or dynamic networks as a tracking problem, where the target state is the ensemble of edges in the network. We used the Kalman filter to track the network topology over time. Unfortunately, the output of the Kalman filter does not reflect known properties of …

Inception Modules Enhance Brain Tumor Segmentation., Daniel E Cahall, Ghulam Rasool, Nidhal Carla Bouaynaya, Hassan M Fathallah-Shaykh

Henry M. Rowan College of Engineering Faculty Scholarship

Magnetic resonance images of brain tumors are routinely used in neuro-oncology clinics for diagnosis, treatment planning, and post-treatment tumor surveillance. Currently, physicians spend considerable time manually delineating different structures of the brain. Spatial and structural variations, as well as intensity inhomogeneity across images, make the problem of computer-assisted segmentation very challenging. We propose a new image segmentation framework for tumor delineation that benefits from two state-of-the-art machine learning architectures in computer vision, i.e., Inception modules and U-Net image segmentation architecture. Furthermore, our framework includes two learning regimes, i.e., learning to segment intra-tumoral structures (necrotic and non-enhancing tumor core, peritumoral edema, …