1 citations · 1 across the 4 of their papers we have counts for
4 papers
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification
Naif Alkhunaizi, Faris Almalik, Rouqaiah Al-Refai +2
With the advent of large pre-trained transformer models, fine-tuning these models for various downstream tasks is a critical problem. Paucity of training data, the existence of dat…
FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack Detection
Naif Alkhunaizi, Koushik Srivatsan, Faris Almalik +2
Lack of generalization to unseen domains/attacks is the Achilles heel of most face presentation attack detection (FacePAD) algorithms. Existing attempts to enhance the generalizabi…
FeSViBS: Federated Split Learning of Vision Transformer with Block Sampling
Faris Almalik, Naif Alkhunaizi, Ibrahim Almakky +1
Data scarcity is a significant obstacle hindering the learning of powerful machine learning models in critical healthcare applications. Data-sharing mechanisms among multiple entit…
Self-Ensembling Vision Transformer (SEViT) for Robust Medical Image Classification
Faris Almalik, Mohammad Yaqub, Karthik Nandakumar
Vision Transformers (ViT) are competing to replace Convolutional Neural Networks (CNN) for various computer vision tasks in medical imaging such as classification and segmentation.…