114 research outputs found
Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks
Accurate detection and localization for angiodysplasia lesions is an
important problem in early stage diagnostics of gastrointestinal bleeding and
anemia. Gold-standard for angiodysplasia detection and localization is
performed using wireless capsule endoscopy. This pill-like device is able to
produce thousand of high enough resolution images during one passage through
gastrointestinal tract. In this paper we present our winning solution for
MICCAI 2017 Endoscopic Vision SubChallenge: Angiodysplasia Detection and
Localization its further improvements over the state-of-the-art results using
several novel deep neural network architectures. It address the binary
segmentation problem, where every pixel in an image is labeled as an
angiodysplasia lesions or background. Then, we analyze connected component of
each predicted mask. Based on the analysis we developed a classifier that
predict angiodysplasia lesions (binary variable) and a detector for their
localization (center of a component). In this setting, our approach outperforms
other methods in every task subcategory for angiodysplasia detection and
localization thereby providing state-of-the-art results for these problems. The
source code for our solution is made publicly available at
https://github.com/ternaus/angiodysplasia-segmentatioComment: 12 pages, 6 figure
Automatic detection of crohn disease in wireless capsule endoscopic images using a deep convolutional neural network
The diagnosis of Crohn’s disease (CD) in the small bowel is generally performed by observing a very large number of images captured by capsule endoscopy (CE). This diagnostic technique entails a heavy workload for the specialists in terms of time spent reviewing the images. This paper presents a convolutional neural network capable of classifying the CE images to identify those ones affected by lesions indicative of the disease. The architecture of the proposed network was custom designed to solve this image classification problem. This allowed different design decisions to be made with the aim of improving its performance in terms of accuracy and processing speed compared to other state-of-the-art deep-learning-based reference architectures. The experimentation was carried out on a set of 15,972 images extracted from 31 CE videos of patients affected by CD, 7,986 of which showed lesions associated with the disease. The training, validation/selection and evaluation of the network was performed on 70%, 10% and 20% of the total images, respectively. The ROC curve obtained on the test image set has an area greater than 0.997, with points in a 95-99% sensitivity range associated with specificities of 99-96%. These figures are higher than those achieved by EfficientNet-B5, VGG-16, Xception or ResNet networks which also require an average processing time per image significantly higher than the one needed in the proposed architecture. Therefore, the network outlined in this paper is proving to be sufficiently promising to be considered for integration into tools used by specialists in their diagnosis of CD. In the sample of images analysed, the network was able to detect 99% of the images with lesions, filtering out for specialist review 96% of those with no signs of disease.Funding for open access charge: Universidad de Huelva / CBUA
This work was part of a project funded under the 2014-2020 Andalusia ERDF Operational Programme (Project Reference: UHU-1257810- PO FEDER 2014-2020
Generic Feature Learning for Wireless Capsule Endoscopy Analysis
The interpretation and analysis of wireless capsule endoscopy (WCE) recordings is a complex task which requires sophisticated computer aided decision (CAD) systems to help physicians with video screening and, finally, with the diagnosis. Most CAD systems used in capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, a new CAD system has to be designed from the scratch. This makes the design of new CAD systems very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which circumvents the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed using state-of-the-art handcrafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase)
The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning
Introduction: Technical burdens and time-intensive review processes limit the
practical utility of video capsule endoscopy (VCE). Artificial intelligence
(AI) is poised to address these limitations, but the intersection of AI and VCE
reveals challenges that must first be overcome. We identified five challenges
to address. Challenge #1: VCE data are stochastic and contains significant
artifact. Challenge #2: VCE interpretation is cost-intensive. Challenge #3: VCE
data are inherently imbalanced. Challenge #4: Existing VCE AIMLT are
computationally cumbersome. Challenge #5: Clinicians are hesitant to accept
AIMLT that cannot explain their process.
Methods: An anatomic landmark detection model was used to test the
application of convolutional neural networks (CNNs) to the task of classifying
VCE data. We also created a tool that assists in expert annotation of VCE data.
We then created more elaborate models using different approaches including a
multi-frame approach, a CNN based on graph representation, and a few-shot
approach based on meta-learning.
Results: When used on full-length VCE footage, CNNs accurately identified
anatomic landmarks (99.1%), with gradient weighted-class activation mapping
showing the parts of each frame that the CNN used to make its decision. The
graph CNN with weakly supervised learning (accuracy 89.9%, sensitivity of
91.1%), the few-shot model (accuracy 90.8%, precision 91.4%, sensitivity
90.9%), and the multi-frame model (accuracy 97.5%, precision 91.5%, sensitivity
94.8%) performed well. Discussion: Each of these five challenges is addressed,
in part, by one of our AI-based models. Our goal of producing high performance
using lightweight models that aim to improve clinician confidence was achieved
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