277 citations · 348 across the 13 of their papers we have counts for
14 papers
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…
A Foundation Model for Spatial Proteomics
Muhammad Shaban, Yuzhou Chang, Huaying Qiu +57
Foundation models have begun to transform image analysis by acting as pretrained generalist backbones that can be adapted to many tasks even when post-training data are limited, ye…
Multimodal Whole Slide Foundation Model for Pathology
Tong Ding, Sophia J. Wagner, Andrew H. Song +20
The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transfe…
A General-Purpose Self-Supervised Model for Computational Pathology
Richard J. Chen, Tong Ding, Ming Y. Lu +17
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole…
Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning
Richard J. Chen, Ming Y. Lu, Drew F. K. Williamson +8
The rapidly emerging field of deep learning-based computational pathology has demonstrated promise in developing objective prognostic models from histology whole slide images. Howe…
Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks
Richard J. Chen, Ming Y. Lu, Muhammad Shaban +4
Cancer prognostication is a challenging task in computational pathology that requires context-aware representations of histology features to adequately infer patient survival. Desp…
A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma
Muhammad Shaban, Shan E Ahmed Raza, Mariam Hassan +11
The infiltration of T-lymphocytes in the stroma and tumour is an indication of an effective immune response against the tumour, resulting in better survival. In this study, our aim…
CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images
Yanning Zhou, Simon Graham, Navid Alemi Koohbanani +3
Colorectal cancer (CRC) grading is typically carried out by assessing the degree of gland formation within histology images. To do this, it is important to consider the overall tis…
Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images
Muhammad Shaban, Ruqayya Awan, Muhammad Moazam Fraz +3
Digital histology images are amenable to the application of convolutional neural network (CNN) for analysis due to the sheer size of pixel data present in them. CNNs are generally…
Methods for Segmentation and Classification of Digital Microscopy Tissue Images
Quoc Dang Vu, Simon Graham, Minh Nguyen Nhat To +11
High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help…
StainGAN: Stain Style Transfer for Digital Histological Images
M Tarek Shaban, Christoph Baur, Nassir Navab +1
Digitized Histological diagnosis is in increasing demand. However, color variations due to various factors are imposing obstacles to the diagnosis process. The problem of stain col…
Micro-Net: A unified model for segmentation of various objects in microscopy images
Shan E Ahmed Raza, Linda Cheung, Muhammad Shaban +5
Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based…
Context-Aware Learning using Transferable Features for Classification of Breast Cancer Histology Images
Ruqayya Awan, Navid Alemi Koohbanani, Muhammad Shaban +2
Convolutional neural networks (CNNs) have been recently used for a variety of histology image analysis. However, availability of a large dataset is a major prerequisite for trainin…
Representation-Aggregation Networks for Segmentation of Multi-Gigapixel Histology Images
Abhinav Agarwalla, Muhammad Shaban, Nasir M. Rajpoot
Convolutional Neural Network (CNN) models have become the state-of-the-art for most computer vision tasks with natural images. However, these are not best suited for multi-gigapixe…