no code implementations • 23 Apr 2024 • Clément Christophe, Praveen K Kanithi, Prateek Munjal, Tathagata Raha, Nasir Hayat, Ronnie Rajan, Ahmed Al-Mahrooqi, Avani Gupta, Muhammad Umar Salman, Gurpreet Gosal, Bhargav Kanakiya, Charles Chen, Natalia Vassilieva, Boulbaba Ben Amor, Marco AF Pimentel, Shadab Khan
This study presents a comprehensive analysis and comparison of two predominant fine-tuning methodologies - full-parameter fine-tuning and parameter-efficient tuning - within the context of medical Large Language Models (LLMs).
1 code implementation • 19 Jun 2020 • Davood Karimi, Lana Vasung, Camilo Jaimes, Fedel Machado-Rivas, Shadab Khan, Simon K. Warfield, Ali Gholipour
Existing methods for estimating the number and orientations of fascicles in an imaging voxel either depend on non-convex optimization techniques that are sensitive to initialization and measurement noise, or are prone to predicting spurious fascicles.
no code implementations • 4 Apr 2020 • Ahmed H. Shahin, Prateek Munjal, Ling Shao, Shadab Khan
We propose a novel approach for effectively encoding the user input from extreme points and corrective clicks, in a novel and scalable manner that allows the network to work with a variable number of clicks, including corrective clicks for output refinement.
2 code implementations • CVPR 2022 • Prateek Munjal, Nasir Hayat, Munawar Hayat, Jamshid Sourati, Shadab Khan
Finally, we conclude with a set of recommendations on how to assess the results using a new AL algorithm to ensure results are reproducible and robust under changes in experimental conditions.
Ranked #6 on Active Learning on CIFAR10 (10,000)
1 code implementation • 6 Jun 2019 • Shadab Khan, Ahmed H. Shahin, Javier Villafruela, Jianbing Shen, Ling Shao
To automate the process of segmenting an anatomy of interest, we can learn a model from previously annotated data.
no code implementations • 15 Mar 2018 • Seyed Sadegh Mohseni Salehi, Shadab Khan, Deniz Erdogmus, Ali Gholipour
Our results show that in such registration applications that are amendable to learning, the proposed deep learning methods with geodesic loss minimization can achieve accurate results with a wide capture range in real-time (<100ms).