EIR — Efficient computer aided diagnosis framework for gastrointestinal endoscopies

Riegler, Michael; Pogorelov, Konstantin; Halvorsen, Pål; Lange, Thomas de; Griwodz, Carsten; Schmidt, Peter T.; Eskeland, Sigrun Losada; Johansen, Dag

doi:10.1109/cbmi.2016.7500257

Cited by 31 publications

(28 citation statements)

References 25 publications

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“…This is most probably due to the lack of alternative data in the medical field, but also that polyps are precursors of CRC. Several algorithms, methods and partial systems have, at first glance, achieved promising results [37] in their respective testing environment. However, none of the related works is able to perform real-time detection or support doctors by computeraided diagnosis in real-time during colonoscopies.…”

Section: Related Workmentioning

confidence: 99%

“…However, none of the related works is able to perform real-time detection or support doctors by computeraided diagnosis in real-time during colonoscopies. Furthermore, all of them are limited to a very specific use case, which in most cases is polyp detection for a specific type of camera [37]. Furthermore, in some cases, it is unclear how well the approach would perform as a real system used in hospitals.…”

Section: Related Workmentioning

confidence: 99%

“…In our previous work, we presented our basic EIR system [32,36,37] that implements a single-class global-feature-based detector able to recognize the abnormalities in a given video frame. Global image features were chosen, because they are easy and fast to calculate, and the exact lesion's position is not needed for detection, i.e., identifying frames that contain a disease.…”

Section: Basic Eir Systemmentioning

confidence: 99%

“…To further aid and scale such examinations, we have earlier presented EIR 1 [32,37], an efficient and scalable automatic analysis and feedback system for medical videos and images. The system is designed to support endoscopists in the detection and interpretation of diseases in the GI tract.…”

Section: Introductionmentioning

confidence: 99%

“…Our first version [32,37] was developed for detection of polyps, i.e., possible cancer precursors, and it was built on content-based information retrieval methodology using global image features for image content analysis. In this paper, the next generation of our system is presented, where we extend our system using out-of-the-box and improved deep learning neural network approaches and multi-class global-feature classification methods for detection and localization of endoscopic findings.…”

Section: Introductionmentioning

confidence: 99%

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Efficient disease detection in gastrointestinal videos – global features versus neural networks

Pogorelov

Riegler

Eskeland

et al. 2017

Multimed Tools Appl

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Analysis of medical videos from the human gastrointestinal (GI) tract for detection and localization of abnormalities like lesions and diseases requires both high precision and recall. Additionally, it is important to support efficient, real-time processing for live feedback during (i) standard colonoscopies and (ii) scalability for massive population-based screening, which we conjecture can be done using a wireless video capsule endoscope (camera-pill). Existing related work in this field does neither provide the necessary combination of accuracy and performance for detecting multiple classes of abnormalities simultaneously nor for particular disease localization tasks. In this paper, a complete end-toend multimedia system is presented where the aim is to tackle automatic analysis of GI tract videos. The system includes an entire pipeline ranging from data collection, processing and analysis, to visualization. The system combines deep learning neural networks, information retrieval, and analysis of global and local image features in order to implement multi-class classification, detection and localization. Furthermore, it is built in a modular way, so that it can be easily extended to deal with other types of abnormalities. Simultaneously, the system is developed for efficient processing in order to provide real-time feedback to the doctors and for scalability reasons when potentially applied for massive population-based algorithmic screenings in the future. Initial experiments show that our system has multiclass detection accuracy and polyp localization precision at least as good as state-of-the-art systems, and provides additional novelty in terms of real-time performance, low resource consumption and ability to extend with support for new classes of diseases.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Basic Eir Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient disease detection in gastrointestinal videos – global features versus neural networks

Pogorelov

Riegler

Eskeland

et al. 2017

Multimed Tools Appl

Self Cite

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The EndoTect 2020 Challenge: Evaluation and Comparison of Classification, Segmentation and Inference Time for Endoscopy

Hicks

Jha

Thambawita

et al. 2021

Lecture Notes in Computer Science

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ECGDT: a graphical software tool for ECG diagnosis

Mondelo,

Lado,

Méndez

2023

Multimed Tools Appl

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While cardiovascular diseases are the leading causes of death in developed countries, detection of cardiac abnormalities can reduce mortality rates, through early and accurate diagnosis. One of the main assets used to help in the diagnosis process is the electrocardiogram (ECG). A free software tool for electrocardiogram analysis and diagnosis is presented. The tool, named ECGDT, allows: (1) to detect beats present on the ECG, both in single and multi-channel levels, (2) to identify beat waves, and (3) to diagnose different cardiac abnormalities. System evaluation was performed in two ways: (1) diagnostic capabilities were tested with Receiver Operating Characteristic (ROC) analysis, and (2) Graphical Software Interface (GUI) aspects, such as attraction, efficiency, or novelty, were evaluated employing User Experience Questionnaire (UEQ) scores. For disease diagnosis, the mean Area Under the ROC Curve (AUC) was 0.821. The system was also capable of detecting 100% of several cardiac abnormalities, such as bradycardia or tachycardia. Related to the GUI, all usability estimators scored values ranged between 2.208 and 2.750 (overall positive evaluations are obtained for values over 0.8). ECGDT could serve as an aid in the diagnosis of different medical abnormalities. In addition, the suitability of the developed interface has been proven.

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EIR — Efficient computer aided diagnosis framework for gastrointestinal endoscopies

Cited by 31 publications

References 25 publications

Efficient disease detection in gastrointestinal videos – global features versus neural networks

Efficient disease detection in gastrointestinal videos – global features versus neural networks

The EndoTect 2020 Challenge: Evaluation and Comparison of Classification, Segmentation and Inference Time for Endoscopy

ECGDT: a graphical software tool for ECG diagnosis

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