Anindya Gupta
Visiting researcher at Institutionen för informationsteknologi; Vi3; Bildanalys
- E-mail:
- anindya.gupta@it.uu.se
- Visiting address:
- Hus 10, Lägerhyddsvägen 1
- Postal address:
- Box 337
751 05 UPPSALA
- ORCID:
- 0000-0003-3557-4947
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Short presentation
Anindya Gupta is a postdoctoral researcher at the Division of Visual Information and Interaction, Department of Information Technology, Uppsala University. His current research involves developing automated approaches for classifying cells based on their dynamic behaviour in time-series of microscopy images using machine/deep learning, convolutional neural networks in particular.
Keywords
- biomedical image analysis
- cell analysis
- convolutional neural networks
- deep learning
- image cytometry
- machine learning
- medical image analysis
Biography
Education
- Ph.D. Computerized Image Analysis, Faculty of Information Technology, Tallinn University of Technology, Tallinn, Estonia, 2018.
- M.Sc. Software Engineering, Kingston University, London, United Kingdom, 2013.
-
B. Tech. (Honors) Computer Science, Rajasthan Technical University, India, 2010.
Research
http://sysmic.ki.se/the-project
Publications
Selection of publications
- Denoising of short exposure transmission electron microscopy images for ultrastructural enhancement (2018)
- Automatic detection of multisize pulmonary nodules in CT images (2018)
- Convolutional neural networks for false positive reduction of automatically detected cilia in low magnification TEM images (2017)
- Classification of cross-sections for vascular skeleton extraction using convolutional neural networks (2017)
Recent publications
- Weakly-supervised prediction of cell migration modes in confocal microscopy images using bayesian deep learning (2020)
- Detection of pulmonary micronodules in computed tomography images and false positive reduction using 3D convolutional neural networks (2020)
- Deep Learning in Image Cytometry (2019)
- Super-resolution Reconstruction of Transmission Electron Microscopy Images using Deep Learning (2019)
- Denoising of short exposure transmission electron microscopy images for ultrastructural enhancement (2018)
All publications
Articles
- Detection of pulmonary micronodules in computed tomography images and false positive reduction using 3D convolutional neural networks (2020)
- Deep Learning in Image Cytometry (2019)
- Automatic detection of multisize pulmonary nodules in CT images (2018)
Conferences
- Weakly-supervised prediction of cell migration modes in confocal microscopy images using bayesian deep learning (2020)
- Super-resolution Reconstruction of Transmission Electron Microscopy Images using Deep Learning (2019)
- Denoising of short exposure transmission electron microscopy images for ultrastructural enhancement (2018)
- Denoising of Short Exposure Transmission Electron Microscopy Images using CNN (2018)
- Convolutional neural networks for false positive reduction of automatically detected cilia in low magnification TEM images (2017)
- Classification of cross-sections for vascular skeleton extraction using convolutional neural networks (2017)
