Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
4 results
Search Results
Item Medical Image Retrieval Using Manifold Ranking with Relevance Feedback(Institute of Electrical and Electronics Engineers Inc., 2018) Soundalgekar, P.; Kulkarni, M.; Nagaraju, D.; Kamath S․, S.Medical image retrieval (MedIR) is a challenging field in Visual information retrieval, due to the multi-dimensional and multi-modal context of the underlying content. Traditional models do not take the intrinsic characteristics of data into consideration and have achieved limited accuracy in application to medical images. Manifold Ranking (MR) is a technique that can be used in further optimizing precision and recall in MedIR applications as it ranks items by traversing a dynamically constructed content-specific information graph. In this paper, a MedIR approach based on Manifold Ranking is proposed. Medical images being multi-dimensional, exhibit underlying cluster and manifold information which enhances semantic relevance and allows for label uniformity. Hence, when adapted for MedIR, MR can help in achieving large-scale ranking across datasets as is the case in most medical imaging applications. In addition, a relevance feedback mechanism was also incorporated to support a learning based system. We show that MR achieved significant improvement in retrieval results with relevance feedback as compared to the Euclidean Distance (ED) rankings. This showcases the importance of analyzing the inherent latent structure in medical image data for better performance over traditional methods. © 2018 IEEE.Item Deep Neural Models for Early Diagnosis of Knee Osteoarthritis and Severity Grade Prediction(Springer Science and Business Media Deutschland GmbH, 2022) Shenoy, T.N.; Medayil, M.; Kamath S․, S.Osteoarthritis (OA) is a type of arthritis that results in malfunction and eventual loss of the cartilage of joints. It occurs when the cartilage that cushions the ends of the bones wear out. OA is the most common joint disease which frequently occurs after the age of 45 in case of males and 55 in the case of females. Manual detection of OA is a tedious and labour-intensive task and is performed by trained specialists. We propose a fully automated computer-aided diagnosis system to detect and grade osteoarthritis severity as per the Kellgren-Lawrence (KL) classification. In this paper, we experiment with various approaches for automated OA detection from X-ray images. Image-level information such as content descriptors and image transforms are identified and assigned weights using Fisher scores. KL-grade is then projected using weighted nearest neighbours, and different stages of OA severity are classified. Pre-processing, segmentation, and classification of the X-ray images are achieved using data augmentation, deep neural network, and residual neural networks. We present experimental results and discussion with respect to the best-performing models in our experiments. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item A Comprehensive Review of Brain Tumor Detection and Segmentation Techniques(Springer Science and Business Media Deutschland GmbH, 2023) Azade, A.; Kumar, P.; Kamath S․, S.Brain tumors are particularly dangerous type of tumor, and if this is not treated in time it maybe prove to be deadly and may also spread across other body parts. Brain tumor is the swelling or growth of unwanted tissues in the brain that results from the unregulated and disordered division of cells. The presence of these tissues resulting abnormal behavior and lot of other complications. The detection of brain tumor is done by using different techniques out of which through magnetic resonance images (MRIs). The scanning process is a time-consuming manual task that needs the involvement of medical professionals. Automating the task of detection of the brain tumor while also grading the severity accurately can help in managing the patients’ disease effectively. As tumor tissue of different patients is different, automating such processes is often a challenging task. Researchers have incorporated image segmentation for extraction of suspicious regions from MRI, using image processing and AI-based techniques. Radiomic analysis also plays a big role in feature extraction processes. In this paper, we present a comprehensive review of existing approaches for brain tumour detection, covering deep neural models, radiomic analysis and segmentation-based methods for brain tumor classification and segmentation, along with a discussion on prevalent issues, challenges, and future directions of research. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Transformer and Knowledge Based Siamese Models for Medical Document Retrieval(Institute of Electrical and Electronics Engineers Inc., 2023) Dash, A.; Merchant, A.M.; Chintawar, S.; Kamath S․, S.Vocabulary mismatch is a significant issue when it comes to query-based document retrieval in the medical field. Since the documents are typically authored by professionals, they may contain many specialized terms that are not widely understood or used. Traditional information retrieval (IR) models like vector space and best match-based models fail in this regard. Neural Learning to Rank (NLtR) and transformer models have attracted significant research attention in the field of IR. Recent works in the medical field utilize medical knowledge bases (KB) that map words to concepts and aid in connecting several words to the same concept. In this paper, we present various Siamese-structured transformer and knowledge-based retrieval models designed to address the retrieval issues in the medical domain. The experimental evaluation highlighted the superior performance of the proposed retrieval model, and the best one, based on the UMLSBert ENG transformer, achieved best-in-class performance with respect to all evaluation metrics. © 2023 IEEE.