Dr. Nishant Ravikumar Profile

Dr. Nishant Ravikumar

Lecturer at Univ of Leeds

SPIE Involvement:

Conference Program Committee | Author

Proceedings Article | 3 April 2024 Poster + Paper

Segmenting cardiac muscle Z-disks with deep neural networks

Mihaela Croitor Ibrahim, Nishant Ravikumar, Alistair Curd, Joanna Leng, Oliver Umney, Michelle Peckham

Proceedings Volume 12933, 129331E (2024) https://doi.org/10.1117/12.3006886

KEYWORDS: Image segmentation, Muscles, Confocal microscopy, Proteins, Data modeling, Cross validation, Tissues, Network architectures, Deep learning

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Proceedings Article | 10 April 2023 Presentation + Paper

Radiology report generation using transformers conditioned with non-imaging data

Nurbanu Aksoy, Nishant Ravikumar, Alejandro Frangi

Proceedings Volume 12469, 124690O (2023) https://doi.org/10.1117/12.2653672

KEYWORDS: Transformers, Visualization, Chest imaging, Radiology

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Medical image interpretation is central to most clinical applications such as disease diagnosis, treatment planning, and prognostication. In clinical practice, radiologists examine medical images (e.g. chest x-rays, computed tomography images, etc.) and manually compile their findings into reports, which can be a time-consuming process. Automated approaches to radiology report generation, therefore, can reduce radiologist workload and improve efficiency in the clinical pathway. While recent deep-learning approaches for automated report generation from medical images have seen some success, most studies have relied on image-derived features alone, ignoring non-imaging patient data. Although a few studies have included the word-level contexts along with the image, the use of patient demographics is still unexplored. On the other hand, prior approaches to this task commonly use encoder-decoder frameworks that consist of a convolution vision model followed by a recurrent language model. Although recurrent-based text generators have achieved noteworthy results, they had the drawback of having a limited reference window and identifying only one part of the image while generating the next word. This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information, to synthesise patient-specific radiology reports. The proposed network uses a convolutional neural network (CNN) to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information, to synthesise full-text radiology reports. The designed network not only alleviates the limitations of the recurrent models but also improves the encoding and generative processes by including more context in the network. Data from two public databases were used to train and evaluate the proposed approach. CXRs and reports were extracted from the MIMIC-CXR database and combined with corresponding patients’ data (gender, age, and ethnicity) from MIMIC-IV. Based on the evaluation metrics used (BLEU 1-4 and BERTScore), including patient demographic information was found to improve the quality of reports generated using the proposed approach, relative to a baseline network trained using CXRs alone. The proposed approach shows potential for enhancing radiology report generation by leveraging rich patient metadata and combining semantic text embeddings derived thereof, with medical image-derived visual features.

Proceedings Article | 7 April 2023 Presentation + Paper

Efficient subtyping of ovarian cancer histopathology whole slide images using active sampling in multiple instance learning

Jack Breen, Katie Allen, Kieran Zucker, Geoff Hall, Nicolas Orsi, Nishant Ravikumar

Proceedings Volume 12471, 1247110 (2023) https://doi.org/10.1117/12.2653869

KEYWORDS: Ovarian cancer, Tissues, Histopathology, Pathology, Diagnostics, Cross validation, Image processing, Data modeling

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Proceedings Article | 3 April 2023 Presentation + Paper

Learning disentangled representations for explainable chest x-ray classification using Dirichlet VAEs

Rachael Harkness, Alejandro Frangi, Kieran Zucker, Nishant Ravikumar

Proceedings Volume 12464, 1246411 (2023) https://doi.org/10.1117/12.2654345

KEYWORDS: Chest imaging, Education and training, Lung, Image classification, Opacity, Visualization, Image restoration, Diseases and disorders, Diagnostics, Binary data

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Proceedings Article | 4 March 2019 Presentation + Paper

Mammographic breast density classification using a deep neural network: assessment based on inter-observer variability

N. Kaiser, A. Fieselmann, S. Vesal, N. Ravikumar, L. Ritschl, S. Kappler, A. Maier

Proceedings Volume 10952, 109520O (2019) https://doi.org/10.1117/12.2513420

KEYWORDS: Breast, Neural networks, Breast cancer, Convolutional neural networks, Image segmentation, Tissues, Image classification, Feature extraction, Mammography, Algorithm development

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Proceedings Article | 21 March 2016 Paper

Robust group-wise rigid registration of point sets using t-mixture model

Nishant Ravikumar, Ali Gooya, Alejandro Frangi, Zeike Taylor

Proceedings Volume 9784, 97840S (2016) https://doi.org/10.1117/12.2216244

KEYWORDS: Rigid registration, 3D modeling, Image segmentation, Medical imaging, Magnetism, Magnetic resonance imaging, Statistical analysis, Statistical modeling, Probability theory, Detection and tracking algorithms, Data modeling, Expectation maximization algorithms, Image registration, Medicine

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Proceedings Article | 20 March 2015 Paper

3D active shape models of human brain structures: application to patient-specific mesh generation

Nishant Ravikumar, Isaac Castro-Mateos, Jose Pozo, Alejandro Frangi, Zeike Taylor

Proceedings Volume 9414, 94142D (2015) https://doi.org/10.1117/12.2082599

KEYWORDS: Brain, Image segmentation, Magnetic resonance imaging, Neuroimaging, Statistical modeling, Image registration, 3D modeling, Principal component analysis, Statistical analysis, Expectation maximization algorithms

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Showing 5 of 7 publications

Conference Committee Involvement (4)

Image Processing

17 February 2025 | San Diego, California, United States

Image Processing

19 February 2024 | San Diego, California, United States

Image Processing

20 February 2023 | San Diego, California, United States

Image Processing

20 February 2022 | San Diego, California, United States

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