19 citations · 22 across the 7 of their papers we have counts for
7 papers
Extending Decoupled Attention to Dense Prediction and Masked Training for Multi-Channel Images
Umar Marikkar, Sameed Husain, Muhammad Awais +1
Multi-Channel imaging (MCI) data differs fundamentally from natural images, as each channel records a semantically distinct signal rather than a colour band. To adapt vision encode…
Domain Adaptation Without the Compute Burden for Efficient Whole Slide Image Analysis
Umar Marikkar, Muhammad Awais, Sara Atito
Computational methods on analyzing Whole Slide Images (WSIs) enable early diagnosis and treatments by supporting pathologists in detection and classification of tumors. However, th…
DC-ViT: Modulating Spatial and Channel Interactions for Multi-Channel Images
Umar Marikkar, Syed Sameed Husain, Muhammad Awais +1
Training and evaluation in multi-channel imaging (MCI) remains challenging due to heterogeneous channel configurations arising from varying staining protocols, sensor types, and ac…
Channel-Aware Probing for Multi-Channel Imaging
Umar Marikkar, Syed Sameed Husain, Muhammad Awais +1
Training and evaluating vision encoders on Multi-Channel Imaging (MCI) data remains challenging as channel configurations vary across datasets, preventing fixed-channel training an…
C3R: Channel Conditioned Cell Representations for unified evaluation in microscopy imaging
Umar Marikkar, Syed Sameed Husain, Muhammad Awais +1
Immunohistochemical (IHC) images reveal detailed information about structures and functions at the subcellular level. However, unlike natural images, IHC datasets pose challenges f…
Review of multimodal machine learning approaches in healthcare
Felix Krones, Umar Marikkar, Guy Parsons +2
Machine learning methods in healthcare have traditionally focused on using data from a single modality, limiting their ability to effectively replicate the clinical practice of int…
LT-ViT: A Vision Transformer for multi-label Chest X-ray classification
Umar Marikkar, Sara Atito, Muhammad Awais +1
Vision Transformers (ViTs) are widely adopted in medical imaging tasks, and some existing efforts have been directed towards vision-language training for Chest X-rays (CXRs). Howev…