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Weekly Research Seminars

This page contains a list with next and coming talks on our weekly seminars in Computer Vision (CV) and Natural Language Processing (NLP).

If you want to be included in the Computer Vision (CV) mailing list, please contact: <alba.garcia(at)essex.ac.uk> or <e.yagis(at)essex.ac.uk>

For past seminars see here.

4 November 2020- CV – Zoom Meeting

Title:  Deep neural ensembles for improved pulmonary abnormality detection in chest radiographs by Sivarama Krishnan Rajaraman

Abstract: Cardiopulmonary diseases account for a significant proportion of deaths and disabilities across the world. Chest X-rays are a common diagnostic imaging modality for confirming intra-thoracic cardiopulmonary abnormalities. However, there remains an acute shortage of expert radiologists, particularly in under-resourced settings that results in interpretation delays and could have global health impact. These issues can be mitigated by an artificial intelligence (AI) powered computer-aided diagnostic (CADx) system. Such a system could help supplement decision-making and improve throughput while preserving and possibly improving the standard-of-care. A majority of such AI-based diagnostic tools at present use data-driven deep learning (DL) models that perform automated feature extraction and classification. Convolutional neural networks (CNN), a class of DL models, have gained significant research prominence in tasks related to image classification, detection, and localization. The literature reveals that they deliver promising results that scale impressively with an increasing number of training samples and computational resources. However,  the techniques may be adversely impacted due to their sensitivity to high variance or fluctuations in training data. Ensemble learning helps mitigate these by combining predictions  and blending intelligence from multiple learning algorithms. Complex non-linear functions constructed within ensembles help improve robustness and generalization. Empirical result predictions have demonstrated superiority over the conventional approach with stand-alone CNN models. In this talk, I will describe example work at the NLM that use model ensembles to improve pulmonary abnormality detection in chest radiographs.

Bio: Dr. Sivaramakrishnan Rajaraman joined the Lister Hill National Center for Biomedical Communications (LHNCBC), National Library of Medicine (NLM), National Institutes of Health (NIH), as a postdoctoral researcher in 2016. Dr. Rajaraman received his Ph.D. in Information and Communication Engineering from Anna University, Chennai, India. He is involved in projects that aim to apply computational sciences and engineering techniques toward advancing life science applications. These projects involve use of medical images for aiding healthcare professionals in low-cost decision-making at the point of care screening/diagnostics. Dr. Rajaraman is a versatile researcher with expertise in machine learning, data science, biomedical image analysis/understanding, and computer vision. He has more than 15 years of experience in academia where he taught core and allied subjects in biomedical engineering. He has authored several national and international journal and conference publications in his area of expertise.

18 November 2020- CV – Zoom Meeting

Title: TBD by Gurkirat Sarna

Abstract: TBD

Bio: TBD