Browsing by Author "Ashwin Nayak, U."

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    BEV Detection and Localisation using Semantic Segmentation in Autonomous Car Driving Systems
    (Institute of Electrical and Electronics Engineers Inc., 2021) Ashwin Nayak, U.; Naganure, N.; Kamath S․, S.
    In autonomous vehicles, the perception system plays an important role in environment modeling and object detection in 3D space. Existing perception systems use various sensors to localize and track the surrounding obstacles, but have some limitations. Most existing end-to-end autonomous systems are computationally heavy as they are built on multiple deep networks that are trained to detect and localize objects, thus requiring custom, high-end computation devices with high compute power. To address this issue, we propose and experiment with different semantic segmentation-based models for Birds Eye View (BEV) detection and localization of surrounding objects like vehicles and pedestrians from LiDAR (light detection, and ranging) point clouds. Voxelisation techniques are used to transform 3D LiDAR point clouds to 2D RGB images. The semantic segmentation models are trained from the ground up on the Lyft Level 5 dataset. During experimental evaluation, the proposed approach achieved a mean average precision score of 0.044 for UNET, 0.041 for SegNet and 0.033 for FCN, while being significantly less compute-intensive when compared to the state-of-the-art approaches. © 2021 IEEE.
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    Leveraging deep learning approaches for patient case similarity evaluation
    (Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Naganure, N.; Ashwin Nayak, U.; Kamath S․, S.
    One of the fundamental problems in Health Informatics is evaluating the clinical similarity between two patients for treatment recommendation. Retrieving clinical records of existing patients who are potentially similar to a newly arrived patient could help a physician in faster diagnosis and recommending informed treatment options, especially in the case of areas where specialist medical care is scarce. In Western countries, patient records are extensively stored in the form of Electronic Health Records (EHR), which are created manually by human experts, which can take a lot of time and is a cost-intensive process. In developing countries like India, patient records are increasingly being stored in digital formats and often contain diverse, heterogeneous, unstructured reports of patients. These can be potentially utilized for designing patient similarity assessment and recommendation systems. In this paper, we propose a patient similarity evaluation framework built on two supervised learning models—Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU). Our method creates an optimal patient representation for existing patients by aggregating reports collected over the duration of treatment, to overcome the loss of temporal information, for which a cohort of 16,723 patients across 8 disease categories was used. Both the models (CNN and GRU) learn by passing through the records of a patient chronologically and achieve an accuracy of 97.60 and 93.62%, respectively, on standard EHR dataset like MIMIC-III. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.