Search Results for author: Jens Rittscher

Found 22 papers, 5 papers with code

Beyond attention: deriving biologically interpretable insights from weakly-supervised multiple-instance learning models

no code implementations7 Sep 2023 Willem Bonnaffé, CRUK ICGC Prostate Group, Freddie Hamdy, Yang Hu, Ian Mills, Jens Rittscher, Clare Verrill, Dan J. Woodcock

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.

Multiple Instance Learning

SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis

no code implementations31 May 2023 Ziang Xu, Jens Rittscher, Sharib Ali

We also demonstrate that our method generalises better than all SOTA methods to unseen datasets, reporting nearly 7% improvement in our generalisability assessment.

Self-Supervised Learning

Patch-level instance-group discrimination with pretext-invariant learning for colitis scoring

no code implementations11 Jul 2022 Ziang Xu, Sharib Ali, Soumya Gupta, Simon Leedham, James E East, Jens Rittscher

Inflammatory bowel disease (IBD), in particular ulcerative colitis (UC), is graded by endoscopists and this assessment is the basis for risk stratification and therapy monitoring.

Representation Learning Self-Supervised Learning +1

EndoUDA: A modality independent segmentation approach for endoscopy imaging

no code implementations12 Jul 2021 Numan Celik, Sharib Ali, Soumya Gupta, Barbara Braden, Jens Rittscher

While, today most segmentation approaches are supervised and only concentrated on a single modality dataset, this work exploits to use a target-independent unsupervised domain adaptation (UDA) technique that is capable to generalize to an unseen target modality.

Segmentation Unsupervised Domain Adaptation

A multi-centre polyp detection and segmentation dataset for generalisability assessment

3 code implementations8 Jun 2021 Sharib Ali, Debesh Jha, Noha Ghatwary, Stefano Realdon, Renato Cannizzaro, Osama E. Salem, Dominique Lamarque, Christian Daul, Michael A. Riegler, Kim V. Anonsen, Andreas Petlund, Pål Halvorsen, Jens Rittscher, Thomas de Lange, James E. East

To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as \textit{PolypGen}) curated by a team of computational scientists and expert gastroenterologists.

Medical Image Segmentation

FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation

1 code implementation31 Mar 2021 Nikhil Kumar Tomar, Debesh Jha, Michael A. Riegler, Håvard D. Johansen, Dag Johansen, Jens Rittscher, Pål Halvorsen, Sharib Ali

We propose a novel architecture called feedback attention network (FANet) that unifies the previous epoch mask with the feature map of the current training epoch.

Hard Attention Image Segmentation +2

Microscopic fine-grained instance classification through deep attention

no code implementations6 Oct 2020 Mengran Fan, Tapabrata Chakrabort, Eric I-Chao Chang, Yan Xu, Jens Rittscher

Fine-grained classification of microscopic image data with limited samples is an open problem in computer vision and biomedical imaging.

Classification Deep Attention +2

Additive Angular Margin for Few Shot Learning to Classify Clinical Endoscopy Images

no code implementations23 Mar 2020 Sharib Ali, Binod Bhattarai, Tae-Kyun Kim, Jens Rittscher

In this work, we propose to use a few-shot learning approach that requires less training data and can be used to predict label classes of test samples from an unseen dataset.

Few-Shot Learning Test

Semantic filtering through deep source separation on microscopy images

1 code implementation2 Sep 2019 Avelino Javer, Jens Rittscher

By their very nature microscopy images of cells and tissues consist of a limited number of object types or components.

Conv2Warp: An unsupervised deformable image registration with continuous convolution and warping

no code implementations16 Aug 2019 Sharib Ali, Jens Rittscher

To address this problem, we propose a novel approach of learning a continuous warp of the source image.

Image Registration

Efficient video indexing for monitoring disease activity and progression in the upper gastrointestinal tract

no code implementations10 May 2019 Sharib Ali, Jens Rittscher

In this study, we propose to use an autoencoder for efficient video compression and fast retrieval of video images.

Image Retrieval Retrieval +2

Ink removal from histopathology whole slide images by combining classification, detection and image generation models

1 code implementation10 May 2019 Sharib Ali, Nasullah Khalid Alham, Clare Verrill, Jens Rittscher

Removal of marker ink from these high-resolution whole slide images is non-trivial and complex problem as they contaminate different regions and in an inconsistent manner.

General Classification Image Generation +1

A deep learning framework for quality assessment and restoration in video endoscopy

no code implementations15 Apr 2019 Sharib Ali, Felix Zhou, Adam Bailey, Barbara Braden, James East, Xin Lu, Jens Rittscher

Given the widespread use of endoscopy in different clinical applications, we contend that the robust and reliable identification of such artifacts and the automated restoration of corrupted video frames is a fundamental medical imaging problem.

Deblurring Image Restoration +1

Improving Whole Slide Segmentation Through Visual Context - A Systematic Study

no code implementations11 Jun 2018 Korsuk Sirinukunwattana, Nasullah Khalid Alham, Clare Verrill, Jens Rittscher

While challenging, the dense segmentation of histology images is a necessary first step to assess changes in tissue architecture and cellular morphology.

General Classification Image Classification +1

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