1 code implementation • 9 Nov 2023 • Rishav Bhardwaj, Janarthanam Jothi Balaji, Vasudevan Lakshminarayanan
In this paper, we propose applying a new technique called Overlapping Windows on Semi-Local Region (OW-SLR) to an image to obtain any arbitrary resolution by taking the coordinates of the semi-local region around a point in the latent space.
no code implementations • 10 May 2023 • Abhijeet Phatak, Aditya Chandra Mandal, Janarthanam Jothi Balaji, Vasudevan Lakshminarayanan
Previously we demonstrated the use of deep learning techniques to accurately detect the pupil pixels (segmentation binary mask) in case of VL images for performing VL pupillometry.
1 code implementation • 22 Nov 2021 • V. K. Viekash, Janarthanam Jothi Balaji, Vasudevan Lakshminarayanan
Various ocular diseases and high myopia influence the anatomical reference point Foveal Avascular Zone (FAZ) dimensions.
no code implementations • 30 Jun 2021 • Vasudevan Lakshminarayanan, Hoda Kherdfallah, Arya Sarkar, J. Jothi Balaji
In the past few years, Artificial Intelligence (AI) based approaches have been used to detect and grade DR.
no code implementations • 8 Feb 2021 • Hardit Singh, Simarjeet Saini, Vasudevan Lakshminarayanan
We propose a new method for training convolutional neural networks which integrates reinforcement learning along with supervised learning and use ti for transfer learning for classification of glaucoma from colored fundus images.
1 code implementation • 26 Jan 2021 • Amitojdeep Singh, Sourya Sengupta, Mohammed Abdul Rasheed, Varadharajan Jayakumar, Vasudevan Lakshminarayanan
Deep learning methods for ophthalmic diagnosis have shown considerable success in tasks like segmentation and classification.
2 code implementations • 24 Jan 2021 • Yuliana Jiménez Gaona, María José Rodriguez-Alvarez, Hector Espinó Morató, Darwin Castillo Malla, Vasudevan Lakshminarayanan
Thus, the aim of this study is to build a deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images.
no code implementations • 21 Jan 2021 • Sam Yu, Vasudevan Lakshminarayanan
Due to the fractal nature of retinal blood vessels, the retinal fractal dimension is a natural parameter for researchers to explore and has garnered interest as a potential diagnostic tool.
no code implementations • 13 Jan 2021 • Darwin castillo, Vasudevan Lakshminarayanan, Maria J. Rodriguez-Alvarez
Medical brain image analysis is a necessary step in Computer Assisted /Aided Diagnosis (CAD) systems.
no code implementations • 30 Sep 2020 • Yuliana Jiménez-Gaona, María José Rodríguez-Álvarez, Vasudevan Lakshminarayanan
This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images.
no code implementations • 26 Sep 2020 • Amitojdeep Singh, J. Jothi Balaji, Mohammed Abdul Rasheed, Varadharajan Jayakumar, Rajiv Raman, Vasudevan Lakshminarayanan
The explanations from 13 different attribution methods were rated by a panel of 14 clinicians for clinical significance.
no code implementations • 28 May 2020 • Amitojdeep Singh, Sourya Sengupta, Vasudevan Lakshminarayanan
Deep learning methods have been very effective for a variety of medical diagnostic tasks and has even beaten human experts on some of those.
no code implementations • 17 Dec 2018 • Peyman Gholami, Priyanka Roy, Mohana Kuppuswamy Parthasarathy, Vasudevan Lakshminarayanan
We have also included 25 normal OCT images with their corresponding ground truth delineations which can be used for an accurate evaluation of OCT image segmentation.
no code implementations • 9 Dec 2018 • Sourya Sengupta, Amitojdeep Singh, Henry A. Leopold, Tanmay Gulati, Vasudevan Lakshminarayanan
An overview of the applications of deep learning in ophthalmic diagnosis using retinal fundus images is presented.
no code implementations • 19 Dec 2017 • Henry A. Leopold, Jeff Orchard, John S. Zelek, Vasudevan Lakshminarayanan
Analysis of retinal fundus images is essential for eye-care physicians in the diagnosis, care and treatment of patients.