27 research outputs found
Patch-level instance-group discrimination with pretext-invariant learning for colitis scoring
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. Presently, endoscopic characterisation is largely
operator dependant leading to sometimes undesirable clinical outcomes for
patients with IBD. We focus on the Mayo Endoscopic Scoring (MES) system which
is widely used but requires the reliable identification of subtle changes in
mucosal inflammation. Most existing deep learning classification methods cannot
detect these fine-grained changes which make UC grading such a challenging
task. In this work, we introduce a novel patch-level instance-group
discrimination with pretext-invariant representation learning (PLD-PIRL) for
self-supervised learning (SSL). Our experiments demonstrate both improved
accuracy and robustness compared to the baseline supervised network and several
state-of-the-art SSL methods. Compared to the baseline (ResNet50) supervised
classification our proposed PLD-PIRL obtained an improvement of 4.75% on
hold-out test data and 6.64% on unseen center test data for top-1 accuracy.Comment: 1
New endoscopic tools in inflammatory bowel disease
Endoscopic remission is now considered the ultimate longâterm goal for treating inflammatory bowel disease (IBD). Recent advances in endoscopic techniques have progressively added new tools to the armamentarium of endoscopists for a deeper assessment and characterisation of the intestinal mucosa. Virtual Electronic chromoendoscopy is widely available in the endoscopic units, leading to a more accurate evaluation of the vascular and mucosal architecture of the colon, reducing the gap with histology, which is considered a favourable longâterm measure. In addition, advanced, sophisticated techniques such as endocytoscope and confocal laser endomicroscopy provide insights into individualised and personalised IBD therapy. Finally, high expectations are placed on the advent of Artificial Intelligence (AI) with promising applications that have the potential to revolutionise IBD diagnosis and management. Here, we discuss stateâofâtheâart of endoscopic techniques and their applicability to accurate assess endoscopic and histological remission, predict response to therapy and detect, characterise and guide treatment of colonic dysplastic lesions. We are seeing the dawn of a new era wherein the applications of these new endoscopic tools, hand in hand with AI, offer the most incredible opportunity to deliver precision medicine to patients with IBD
SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis
Data-driven methods have shown tremendous progress in medical image analysis.
In this context, deep learning-based supervised methods are widely popular.
However, they require a large amount of training data and face issues in
generalisability to unseen datasets that hinder clinical translation.
Endoscopic imaging data incorporates large inter- and intra-patient variability
that makes these models more challenging to learn representative features for
downstream tasks. Thus, despite the publicly available datasets and datasets
that can be generated within hospitals, most supervised models still
underperform. While self-supervised learning has addressed this problem to some
extent in natural scene data, there is a considerable performance gap in the
medical image domain. In this paper, we propose to explore patch-level
instance-group discrimination and penalisation of inter-class variation using
additive angular margin within the cosine similarity metrics. Our novel
approach enables models to learn to cluster similar representative patches,
thereby improving their ability to provide better separation between different
classes. Our results demonstrate significant improvement on all metrics over
the state-of-the-art (SOTA) methods on the test set from the same and diverse
datasets. We evaluated our approach for classification, detection, and
segmentation. SSL-CPCD achieves 79.77% on Top 1 accuracy for ulcerative colitis
classification, 88.62% on mAP for polyp detection, and 82.32% on dice
similarity coefficient for segmentation tasks are nearly over 4%, 2%, and 3%,
respectively, compared to the baseline architectures. We also demonstrate that
our method generalises better than all SOTA methods to unseen datasets,
reporting nearly 7% improvement in our generalisability assessment.Comment: 1
Detecting Crohnâs disease from high resolution endoscopy videos: the thick data approach
Detecting diseases in high resolution endoscopy videos can be done in several ways
depending on the methodology for detection. One such method that has been a hot topic
in the field of medical technology research is the implementation of machine learning
techniques to aid in the diagnosis of networks. While, this has been studied extensively
with traditional machine learning methods and more recently neural networks, major
issues persist in their implementation in everyday health. Among the largest issues is the
size of the training data needed to make accurate prediction, as well as the inability to
generalize the networks to several disease. We address these issues with a novel
approach to detecting Inflammatory bowel diseases, specifically Crohnâs disease in
endoscopy videos. We use thick data analytics to teach a network to detect heuristics of
a disease, not to simply make classifications from images. Using heuristic annotations
like bounding boxes and segmentation masks, we train a Siamese neural network to
detect video frames for ulcers, polyps, erosions, and erythema with accuracies as high
as 87.5% for polyps and 77.5% for ulcers. We then implement this network in a protype
frontend that physicians can use to upload videos and receive the processed images in
an interactive format. We also pontificate as to how our approach and prototype can be
expanded to several diseases with learning of more heuristics
Artificial intelligence in gastroenterology: a state-of-the-art review
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett's esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.Cellular mechanisms in basic and clinical gastroenterology and hepatolog
VR-Caps: A Virtual Environment for Capsule Endoscopy
Current capsule endoscopes and next-generation robotic capsules for diagnosis
and treatment of gastrointestinal diseases are complex cyber-physical platforms
that must orchestrate complex software and hardware functions. The desired
tasks for these systems include visual localization, depth estimation, 3D
mapping, disease detection and segmentation, automated navigation, active
control, path realization and optional therapeutic modules such as targeted
drug delivery and biopsy sampling. Data-driven algorithms promise to enable
many advanced functionalities for capsule endoscopes, but real-world data is
challenging to obtain. Physically-realistic simulations providing synthetic
data have emerged as a solution to the development of data-driven algorithms.
In this work, we present a comprehensive simulation platform for capsule
endoscopy operations and introduce VR-Caps, a virtual active capsule
environment that simulates a range of normal and abnormal tissue conditions
(e.g., inflated, dry, wet etc.) and varied organ types, capsule endoscope
designs (e.g., mono, stereo, dual and 360{\deg}camera), and the type, number,
strength, and placement of internal and external magnetic sources that enable
active locomotion. VR-Caps makes it possible to both independently or jointly
develop, optimize, and test medical imaging and analysis software for the
current and next-generation endoscopic capsule systems. To validate this
approach, we train state-of-the-art deep neural networks to accomplish various
medical image analysis tasks using simulated data from VR-Caps and evaluate the
performance of these models on real medical data. Results demonstrate the
usefulness and effectiveness of the proposed virtual platform in developing
algorithms that quantify fractional coverage, camera trajectory, 3D map
reconstruction, and disease classification.Comment: 18 pages, 14 figure
Kvasir-Capsule, a video capsule endoscopy dataset
Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology
Artificial intelligence and inflammatory bowel disease: practicalities and future prospects
Artificial intelligence (AI) is an emerging technology predicted to have significant applications in healthcare. This review highlights AI applications that impact the patient journey in inflammatory bowel disease (IBD), from genomics to endoscopic applications in disease classification, stratification and self-monitoring to risk stratification for personalised management. We discuss the practical AI applications currently in use while giving a balanced view of concerns and pitfalls and look to the future with the potential of where AI can provide significant value to the care of the patient with IBD