405 citations · 742 across the 16 of their papers we have counts for
12 papers · 1 filter
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…
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…
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…
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…