13 citations · 29 across the 11 of their papers we have counts for
12 papers
Endo-NeRF++: Uncertainty-Aware Neural Rendering with Multi-Resolution Hash Encoding for Dynamic Surgical Scene Reconstruction
Gousia Habib, Laura Ruotsalainen
Reconstructing dynamic surgical scenes is crucial for robot-assisted minimally invasive surgery; however, it continues to be difficult because of tissue deformation, occlusions, sp…
XEns-CKD: An Explainable Ensemble-Based Approach for Chronic Kidney Disease Stage Detection
Rehan Ahmad, Gousia Habib, Muhammad Shaban +1
Chronic kidney disease (CKD) is a silent disease. Its progression may not significantly hamper a person's daily routine. Human kidney function can be classified as normal or as one…
medR: Reward Engineering for Clinical Offline Reinforcement Learning via Tri-Drive Potential Functions
Qianyi Xu, Gousia Habib, Feng Wu +5
Reinforcement Learning (RL) offers a powerful framework for optimizing dynamic treatment regimes (DTRs). However, clinical RL is fundamentally bottlenecked by reward engineering: t…
CFL-SparseMed: Communication-Efficient Federated Learning for Medical Imaging with Top-k Sparse Updates
Gousia Habib, Aniket Bhardwaj, Ritvik Sharma +2
Secure and reliable medical image classification is crucial for effective patient treatment, but centralized models face challenges due to data and privacy concerns. Federated Lear…
Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AI
Dilruk Perera, Gousia Habib, Qianyi Xu +4
Reinforcement learning (RL) marks a fundamental shift in how artificial intelligence is applied in healthcare. Instead of merely predicting outcomes, RL actively decides interventi…
medDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support
Qianyi Xu, Gousia Habib, Feng Wu +2
Timely and personalized treatment decisions are essential across a wide range of healthcare settings where patient responses can vary significantly and evolve over time. Clinical d…
Optimizing Vision Transformers with Data-Free Knowledge Transfer
Gousia Habib, Damandeep Singh, Ishfaq Ahmad Malik +1
The groundbreaking performance of transformers in Natural Language Processing (NLP) tasks has led to their replacement of traditional Convolutional Neural Networks (CNNs), owing to…
Harnessing The Power of Attention For Patch-Based Biomedical Image Classification
Gousia Habib, Shaima Qureshi, Malik ishfaq
Biomedical image analysis is of paramount importance for the advancement of healthcare and medical research. Although conventional convolutional neural networks (CNNs) are frequent…
Exploring the Efficacy of Group-Normalization in Deep Learning Models for Alzheimer's Disease Classification
Gousia Habib, Ishfaq Ahmed Malik, Jameel Ahmad +2
Batch Normalization is an important approach to advancing deep learning since it allows multiple networks to train simultaneously. A problem arises when normalizing along the batch…
A Comprehensive Review of Knowledge Distillation in Computer Vision
Gousia Habib, Tausifa jan Saleem, Sheikh Musa Kaleem +2
Deep learning techniques have been demonstrated to surpass preceding cutting-edge machine learning techniques in recent years, with computer vision being one of the most prominent…
LIB-KD: Teaching Inductive Bias for Efficient Vision Transformer Distillation and Compression
Gousia Habib, Tausifa Jan Saleem, Ishfaq Ahmad Malik +1
With the rapid development of computer vision, Vision Transformers (ViTs) offer the tantalising prospect of unified information processing across visual and textual domains due to…
Knowledge Distillation in Vision Transformers: A Critical Review
Gousia Habib, Tausifa Jan Saleem, Brejesh Lall
In Natural Language Processing (NLP), Transformers have already revolutionized the field by utilizing an attention-based encoder-decoder model. Recently, some pioneering works have…