A Deep Learning Approach for Red Lesions Detection in Video Capsule Endoscopies

Coelho, Paulo; Pereira, Ana; Leite, Argentina; Salgado, Marta; Cunha, António

doi:10.1007/978-3-319-93000-8_63

Cited by 32 publications

(21 citation statements)

References 15 publications

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“…Various approaches those are used for the development of automatic bleeding detection methods are VOLUME 8, 2020 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ based on suspected blood indicator [3], statistical features [8], pixel intensity histogram-based features [5], [9], [22], block-based approaches [6], bag-of-words (BOW) based approach [7], salient-point based approaches, [12], [23] and deep learning architectures [10], [11]. Moreover, computeraided ulcer and erosion detection methods are developed using convolutional neural network (CNN) based architecture [15], completed local binary pattern (LBP), and laplacian pyramid [14], and indexed image based approach [16].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, different segmentation schemes are proposed to highlight particular types of diseased lesions. For example, deep learning-based schemes are proposed to segment bleeding in [27] using SegNet, to segment mucosa in [28] using CNN, to segment Angiodysplasia in [29] using CNN encoder-decoder architecture, Esophageal Cancer in [30] using Deeplabv3+ network, and red lesions in [10] using U-net. These deep methods require extensive training using a lot of images for region segmentation, and different types of deep models are trained for capturing different types of disease characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Multiple Linear Discriminant Models for Extracting Salient Characteristic Patterns in Capsule Endoscopy Images for Multi-Disease Detection

Kundu

Fattah

Wahid

2020

IEEE J. Transl. Eng. Health Med.

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Background: Computer-aided disease detection schemes from wireless capsule endoscopy (WCE) videos have received great attention by the researchers for reducing physicians' burden due to the time-consuming and risky manual review process. While single disease classification schemes are greatly dealt by the researchers in the past, developing a unified scheme which is capable of detecting multiple gastrointestinal (GI) diseases is very challenging due to the highly irregular behavior of diseased images in terms of color patterns. Method: In this paper, a computer-aided method is developed to detect multiple GI diseases from WCE videos utilizing linear discriminant analysis (LDA) based region of interest (ROI) separation scheme followed by a probabilistic model fitting approach. Commonly in training phase, as pixel-labeled images are available in small number, only the image-level annotations are used for detecting diseases in WCE images, whereas pixel-level knowledge, although a major source for learning the disease characteristics, is left unused. In view of learning the characteristic disease patterns from pixel-labeled images, a set of LDA models are trained which are later used to extract the salient ROI from WCE images both in training and testing stages. The intensity patterns of ROI are then modeled by a suitable probability distribution and the fitted parameters of the distribution are utilized as features in a supervised cascaded classification scheme. Results: For the purpose of validation of the proposed multi-disease detection scheme, a set of pixel-labeled images of bleeding, ulcer and tumor are used to extract the LDA models and then, a large WCE dataset is used for training and testing. A high level of accuracy is achieved even with a small number of pixel-labeled images. Conclusion: Therefore, the proposed scheme is expected to help physicians in reviewing a large number of WCE images to diagnose different GI diseases.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiple Linear Discriminant Models for Extracting Salient Characteristic Patterns in Capsule Endoscopy Images for Multi-Disease Detection

Kundu

Fattah

Wahid

2020

IEEE J. Transl. Eng. Health Med.

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“…The data set of 3,295+600 images was obtained from [26] as shown in table I. The images are representative of the medical application scenario and include normal as well as bleeding cases.…”

Section: B Image Data Setmentioning

confidence: 99%

“…For fast and consistent computation, all 3,895 images have been re-sized to 150 × 150 pixels. The labels of the images were created based on the segmentation of the same data set as performed by [26]. The sample CE images from the data set are shown in Figure 5.…”

Section: B Image Data Setmentioning

confidence: 99%

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Explaining Machine Learning-Based Classifications of In-Vivo Gastral Images

Malhi

Kampik

Pannu

et al. 2019

2019 Digital Image Computing: Techniques and Applications (DICTA)

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This paper proposes an explainable machine learning tool that can potentially be used for decision support in medical image analysis scenarios. For a decision-support system it is important to be able to reverse-engineer the impact of features on the final decision outcome. In the medical domain, such functionality is typically required to allow applying machine learning to clinical decision making. In this paper, we present initial experiments that have been performed on in-vivo gastral images obtained from capsule endoscopy. Quantitative analysis has been performed to evaluate the utility of the proposed method. Convolutional neural networks have been used for training the validating of the image data set to provide the bleeding classifications. The visual explanations have been provided in the images to help health professionals trust the black box predictions. While the paper focuses on the in-vivo gastral image use case, most findings are generalizable.

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ciu.image: An R Package for Explaining Image Classification with Contextual Importance and Utility

Främling

Knapič

Malhi

2021

Explainable and Transparent AI and Multi-Agent Systems

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Many techniques have been proposed in recent years that attempt to explain results of image classifiers, notably for the case when the classifier is a deep neural network. This paper presents an implementation of the Contextual Importance and Utility method for explaining image classifications. It is an R package that can be used with the most usual image classification models. The paper shows results for typical benchmark images, as well as for a medical data set of gastroenterological images. For comparison, results produced by the LIME method are included. Results show that CIU produces similar or better results than LIME with significantly shorter calculation times. However, the main purpose of this paper is to bring the existence of this package to general knowledge and use, rather than comparing with other explanation methods.

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A Deep Learning Approach for Red Lesions Detection in Video Capsule Endoscopies

Cited by 32 publications

References 15 publications

Multiple Linear Discriminant Models for Extracting Salient Characteristic Patterns in Capsule Endoscopy Images for Multi-Disease Detection

Multiple Linear Discriminant Models for Extracting Salient Characteristic Patterns in Capsule Endoscopy Images for Multi-Disease Detection

Explaining Machine Learning-Based Classifications of In-Vivo Gastral Images

ciu.image: An R Package for Explaining Image Classification with Contextual Importance and Utility

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