405 citations · 742 across the 16 of their papers we have counts for
18 papers
Beyond attention: deriving biologically interpretable insights from weakly-supervised multiple-instance learning models
Willem Bonnaffé, CRUK ICGC Prostate Group, Freddie Hamdy +5
Recent advances in attention-based multiple instance learning (MIL) have improved our insights into the tissue regions that models rely on to make predictions in digital pathology.…
EndoUDA: A modality independent segmentation approach for endoscopy imaging
Numan Celik, Sharib Ali, Soumya Gupta +2
Gastrointestinal (GI) cancer precursors require frequent monitoring for risk stratification of patients. Automated segmentation methods can help to assess risk areas more accuratel…
Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy
Soumya Gupta, Sharib Ali, Louise Goldsmith +2
Kidney stones represent a considerable burden for public health-care systems. Ureteroscopy with laser lithotripsy has evolved as the most commonly used technique for the treatment…
FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation
Nikhil Kumar Tomar, Debesh Jha, Michael A. Riegler +5
The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of biomedical image analysis. Imag…
Unsupervised Adversarial Domain Adaptation For Barrett's Segmentation
Numan Celik, Soumya Gupta, Sharib Ali +1
Barrett's oesophagus (BE) is one of the early indicators of esophageal cancer. Patients with BE are monitored and undergo ablation therapies to minimise the risk, thereby making it…
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
Debesh Jha, Sharib Ali, Nikhil Kumar Tomar +5
Computer-aided detection, localisation, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and…
Microscopic fine-grained instance classification through deep attention
Mengran Fan, Tapabrata Chakrabort, Eric I-Chao Chang +2
Fine-grained classification of microscopic image data with limited samples is an open problem in computer vision and biomedical imaging. Deep learning based vision systems mostly d…
Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy
Sharib Ali, Mariia Dmitrieva, Noha Ghatwary +34
The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy…
Additive Angular Margin for Few Shot Learning to Classify Clinical Endoscopy Images
Sharib Ali, Binod Bhattarai, Tae-Kyun Kim +1
Endoscopy is a widely used imaging modality to diagnose and treat diseases in hollow organs as for example the gastrointestinal tract, the kidney and the liver. However, due to var…
Endoscopy disease detection challenge 2020
Sharib Ali, Noha Ghatwary, Barbara Braden +7
Whilst many technologies are built around endoscopy, there is a need to have a comprehensive dataset collected from multiple centers to address the generalization issues with most…
Semantic filtering through deep source separation on microscopy images
Avelino Javer, Jens Rittscher
By their very nature microscopy images of cells and tissues consist of a limited number of object types or components. In contrast to most natural scenes, the composition is known…
Conv2Warp: An unsupervised deformable image registration with continuous convolution and warping
Sharib Ali, Jens Rittscher
Recent successes in deep learning based deformable image registration (DIR) methods have demonstrated that complex deformation can be learnt directly from data while reducing compu…
Ink removal from histopathology whole slide images by combining classification, detection and image generation models
Sharib Ali, Nasullah Khalid Alham, Clare Verrill +1
Histopathology slides are routinely marked by pathologists using permanent ink markers that should not be removed as they form part of the medical record. Often tumour regions are…
Efficient video indexing for monitoring disease activity and progression in the upper gastrointestinal tract
Sharib Ali, Jens Rittscher
Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. While the endoscopy video contains a wealth of information, tools to cap…
Endoscopy artifact detection (EAD 2019) challenge dataset
Sharib Ali, Felix Zhou, Christian Daul +9
Endoscopic artifacts are a core challenge in facilitating the diagnosis and treatment of diseases in hollow organs. Precise detection of specific artifacts like pixel saturations,…
A deep learning framework for quality assessment and restoration in video endoscopy
Sharib Ali, Felix Zhou, Adam Bailey +4
Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. Artifacts such as motion blur, bubbles, specular reflections, floating o…
Foreign Object Detection and Quantification of Fat Content Using A Novel Multiplexing Electric Field Sensor
Anne E. Rittscher, Aslam Sulaimalebbe, Yves Capdeboscq +1
There is an ever growing need to ensure the quality of food and assess specific quality parameters in all the links of the food chain, ranging from processing, distribution and ret…
Improving Whole Slide Segmentation Through Visual Context - A Systematic Study
Korsuk Sirinukunwattana, Nasullah Khalid Alham, Clare Verrill +1
While challenging, the dense segmentation of histology images is a necessary first step to assess changes in tissue architecture and cellular morphology. Although specific convolut…