Look-behind fully convolutional neural network for computer-aided endoscopy

Diamantis, Dimitrios E.; Iakovidis, Dimitris K.; Koulaouzidis, Anastasios

doi:10.1016/j.bspc.2018.12.005

Cited by 44 publications

(23 citation statements)

References 30 publications

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“…Another approach to reduce the annotation effort with promising results in CE is transfer learning. This type of learning includes the application of ML systems that are pretrained on large datasets of nonendoscopic images for lesion detection in endoscopy images 10 …”

Section: A Summary Of Artificial Intelligence Methods For Capsule Endmentioning

confidence: 99%

Artificial intelligence in small bowel capsule endoscopy ‐ current status, challenges and future promise

Dray

Iakovidis

Houdeville

et al. 2021

J of Gastro and Hepatol

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Neural network‐based solutions are under development to alleviate physicians from the tedious task of small‐bowel capsule endoscopy reviewing. Computer‐assisted detection is a critical step, aiming to reduce reading times while maintaining accuracy. Weakly supervised solutions have shown promising results; however, video‐level evaluations are scarce, and no prospective studies have been conducted yet. Automated characterization (in terms of diagnosis and pertinence) by supervised machine learning solutions is the next step. It relies on large, thoroughly labeled databases, for which preliminary “ground truth” definitions by experts are of tremendous importance. Other developments are under ways, to assist physicians in localizing anatomical landmarks and findings in the small bowel, in measuring lesions, and in rating bowel cleanliness. It is still questioned whether artificial intelligence will enter the market with proprietary, built‐in or plug‐in software, or with a universal cloud‐based service, and how it will be accepted by physicians and patients.

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Section: A Summary Of Artificial Intelligence Methods For Capsule Endmentioning

confidence: 99%

Artificial intelligence in small bowel capsule endoscopy ‐ current status, challenges and future promise

Dray

Iakovidis

Houdeville

et al. 2021

J of Gastro and Hepatol

Self Cite

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“…In VPS, we make use of a state-of-the-art CNN architecture named Look Behind Fully Convolutional Network light or LB-FCN light [45], which offers high object recognition accuracy, while maintaining low computational complexity. Its architecture is based on the original LB-FCN architecture [46], which offers multi-scale feature extraction and shortcut connections that enhance the overall object recognition capabilities. LB-FCN light replaces the original convolutional layers with depth-wise separable convolutions and improves the overall architecture by extracting features under three different sizes (3 × 3, 5 × 5, and 7 × 7), lowering the number of free parameters of the original architecture.…”

Section: Object Recognitionmentioning

confidence: 99%

Uncertainty-Aware Visual Perception System for Outdoor Navigation of the Visually Challenged

Dimas

Diamantis

Kalozoumis

et al. 2020

Sensors

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Every day, visually challenged people (VCP) face mobility restrictions and accessibility limitations. A short walk to a nearby destination, which for other individuals is taken for granted, becomes a challenge. To tackle this problem, we propose a novel visual perception system for outdoor navigation that can be evolved into an everyday visual aid for VCP. The proposed methodology is integrated in a wearable visual perception system (VPS). The proposed approach efficiently incorporates deep learning, object recognition models, along with an obstacle detection methodology based on human eye fixation prediction using Generative Adversarial Networks. An uncertainty-aware modeling of the obstacle risk assessment and spatial localization has been employed, following a fuzzy logic approach, for robust obstacle detection. The above combination can translate the position and the type of detected obstacles into descriptive linguistic expressions, allowing the users to easily understand their location in the environment and avoid them. The performance and capabilities of the proposed method are investigated in the context of safe navigation of VCP in outdoor environments of cultural interest through obstacle recognition and detection. Additionally, a comparison between the proposed system and relevant state-of-the-art systems for the safe navigation of VCP, focused on design and user-requirements satisfaction, is performed.

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“…13 In addition, several DL-based methods have been proposed for medical gastroscopy image analysis. Dimitrios et al 14 proposed a novel fully convolutional neural network (FCN) architecture for the detection of abnormalities such as polyps, ulcers and blood in GI endoscopy images. Furthermore, Ghosh et al 15 introduced an automatic bleeding zone detection method via SegNet.…”

Section: Introductionmentioning

confidence: 99%

Content‐based gastric image retrieval using convolutional neural networks

Zheng

et al. 2020

Int J Imaging Syst Tech

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The endoscopy procedure has demonstrated great efficiency in detecting stomach lesions, with extensive numbers of endoscope images produced globally each day. The content-based gastric image retrieval (CBGIR) system has demonstrated substantial potential in gastric image analysis. Gastric precancerous diseases (GPD) have higher prevalence in gastric cancer patients. Thus, effective intervention is crucial at the GPD stage. In this paper, a CBGIR method is proposed using a modified ResNet-18 to generate binary hash codes for a rapid and accurate image retrieval process. We tested several popular models (AlexNet, VGGNet and ResNet), with ResNet-18 determined as the optimum option. Our proposed method was valued using a GPD data set, resulting in a classification accuracy of 96.21 ± 0.66% and a mean average precision of 0.927 ± 0.006, outperforming other state-of-art conventional methods. Furthermore, we constructed a Gastric-Map (GM) based on feature representations in order to visualize the retrieval results. This work has great auxiliary significance for endoscopists in terms of understanding the typical GPD characteristics and improving aided diagnosis.

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Look-behind fully convolutional neural network for computer-aided endoscopy

Cited by 44 publications

References 30 publications

Artificial intelligence in small bowel capsule endoscopy ‐ current status, challenges and future promise

Artificial intelligence in small bowel capsule endoscopy ‐ current status, challenges and future promise

Uncertainty-Aware Visual Perception System for Outdoor Navigation of the Visually Challenged

Content‐based gastric image retrieval using convolutional neural networks

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