no code implementations • 25 Feb 2024 • Arnav Mishra, Aditi Shetkar, Ganesh M. Bapat, Rajdeep Ojha, Tanmay Tulsidas Verlekar
The ground truth highlights the statistical significance of seven features captured using motion capture systems to differentiate between the two gaits.
no code implementations • 4 Mar 2023 • Shreyas Bhat Brahmavar, Rohit Rajesh, Tirtharaj Dash, Lovekesh Vig, Tanmay Tulsidas Verlekar, Md Mahmudul Hasan, Tariq Khan, Erik Meijering, Ashwin Srinivasan
Deep neural network (DNN) models for retinopathy have estimated predictive accuracies in the mid-to-high 90%.
1 code implementation • 20 Feb 2023 • Soham Rohit Chitnis, Sidong Liu, Tirtharaj Dash, Tanmay Tulsidas Verlekar, Antonio Di Ieva, Shlomo Berkovsky, Lovekesh Vig, Ashwin Srinivasan
To investigate the effect of domain-specific pre-training, we considered the current state-of-the-art multiple-instance learning models, 1) CLAM, an attention-based model, and 2) TransMIL, a self-attention-based model, and evaluated the models' confidence and predictive performance in detecting primary brain tumors - gliomas.
no code implementations • 4 May 2021 • Pedro Albuquerque, Joao Machado, Tanmay Tulsidas Verlekar, Luis Ducla Soares, Paulo Lobato Correia
This paper presents a new dataset called GAIT-IT, captured from 21 subjects simulating 4 gait pathologies, with 2 severity levels, besides normal gait, being considerably larger than publicly available gait pathology datasets, allowing to train a deep learning model for gait pathology classification.