DSI-Net: Deep Synergistic Interaction Network for Joint Classification and Segmentation With Endoscope Images

Zhu, Meilu; Chen, Zhen; Yuan, Yixuan

doi:10.1109/tmi.2021.3083586

Cited by 30 publications

(10 citation statements)

References 45 publications

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“…The proposed VENet was compared against state-of-the-art multi-task learning methods in this experiment. Specifically, four different methodologies were tested, namely MTCSN 35 , DSI-Net 36 , Le et. al.…”

Section: Methodsmentioning

confidence: 99%

“…Second, CVD presents a wide range of sizes and shapes, ranging from small vessels with 1 mm of caliber to varicose veins often protruding from the skin to skin ulcers with well-defined features. Interestingly, and although not validated in CVD, for high variable segmentation/classification situations, multi-task learning techniques have recently demonstrated their added value to empower network training [32][33][34][35][36] . These methodologies combine information from different tasks, boosting the generalization ability of the network, and showing improvement in the overall method's performance.…”

mentioning

confidence: 99%

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A multi-task convolutional neural network for classification and segmentation of chronic venous disorders

Oliveira

Torres

Morais

et al. 2023

Sci Rep

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Chronic Venous Disorders (CVD) of the lower limbs are one of the most prevalent medical conditions, affecting 35% of adults in Europe and North America. Due to the exponential growth of the aging population and the worsening of CVD with age, it is expected that the healthcare costs and the resources needed for the treatment of CVD will increase in the coming years. The early diagnosis of CVD is fundamental in treatment planning, while the monitoring of its treatment is fundamental to assess a patient’s condition and quantify the evolution of CVD. However, correct diagnosis relies on a qualitative approach through visual recognition of the various venous disorders, being time-consuming and highly dependent on the physician’s expertise. In this paper, we propose a novel automatic strategy for the joint segmentation and classification of CVDs. The strategy relies on a multi-task deep learning network, denominated VENet, that simultaneously solves segmentation and classification tasks, exploiting the information of both tasks to increase learning efficiency, ultimately improving their performance. The proposed method was compared against state-of-the-art strategies in a dataset of 1376 CVD images. Experiments showed that the VENet achieved a classification performance of 96.4%, 96.4%, and 97.2% for accuracy, precision, and recall, respectively, and a segmentation performance of 75.4%, 76.7.0%, 76.7% for the Dice coefficient, precision, and recall, respectively. The joint formulation increased the robustness of both tasks when compared to the conventional classification or segmentation strategies, proving its added value, mainly for the segmentation of small lesions.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

A multi-task convolutional neural network for classification and segmentation of chronic venous disorders

Oliveira

Torres

Morais

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The emergence of open datasets is conducive to development in this direction. Many studies have sought to identify only one or several types [20], [21], [22], [23], [24] of abnormalities, such as bleeding [25], [26], tumors [26], polyps [27], ulcers [28], and hookworms [29]. In addition to the abnormalities, several GI anatomical landmarks and low-quality categories were also classified to help diagnose WCE.…”

Section: Wce Abnormality Detectionmentioning

confidence: 99%

FIAS3: Frame Importance-Assisted Sparse Subset Selection to Summarize Wireless Capsule Endoscopy Videos

Xie

Chen

et al. 2023

IEEE Access

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Wireless capsule endoscopy (WCE) is a recently developed tool that allows for the painless and non-invasive examination of the entire gastrointestinal (GI) tract. The microcamera captures a large number of redundant frames for each WCE examination such that a video summarization technique is needed to assist in diagnosis. However, prevalent methods of summarizing WCE videos focus only on the representativeness of the frames owing to a lack of high-level information on their importance. This paper develops a Frame Importance-Assisted Sparse Subset Selection model, called FIAS3, to integrate the high-level frame importance from networks into a sparse subset selection model. The FIAS3 is optimized under three constraints: 1) a frame importance matrix to help pay more attention to important frames, 2) a sparsity constraint to make video summaries more compact, and 3) a similarity-inhibiting constraint to reduce redundancy. The results of experiments on a public dataset demonstrated that our FIAS3 outperforms other methods of summarizing WCE videos. Specifically, its coverage and video reconstruction error were 92% and 0.143, respectively, at a 90% compression ratio, recording respective at least 16.9% and 0.031 improvements over other methods. The results of generalization experiments showed that FIAS3 also achieves competitive results on private datasets. INDEX TERMSComputer-aided diagnosis, deep learning, keyframe extraction, video summarization, wireless capsule endoscopy (WCE).

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“…The second stage involves the application of background erasing and foreground mining. The concept of background erasing and foreground mining is inspired from DSI-Net [ 37 ]. This stage uses the features from the first fire block as a base feature map.…”

Section: Proposed Systemmentioning

confidence: 99%

A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images

et al. 2022

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The first step in the diagnosis of gastric abnormalities is the detection of various abnormalities in the human gastrointestinal tract. Manual examination of endoscopy images relies on a medical practitioner’s expertise to identify inflammatory regions on the inner surface of the gastrointestinal tract. The length of the alimentary canal and the large volume of images obtained from endoscopic procedures make traditional detection methods time consuming and laborious. Recently, deep learning architectures have achieved better results in the classification of endoscopy images. However, visual similarities between different portions of the gastrointestinal tract pose a challenge for effective disease detection. This work proposes a novel system for the classification of endoscopy images by focusing on feature mining through convolutional neural networks (CNN). The model presented is built by combining a state-of-the-art architecture (i.e., EfficientNet B0) with a custom-built CNN architecture named Effimix. The proposed Effimix model employs a combination of squeeze and excitation layers and self-normalising activation layers for precise classification of gastrointestinal diseases. Experimental observations on the HyperKvasir dataset confirm the effectiveness of the proposed architecture for the classification of endoscopy images. The proposed model yields an accuracy of 97.99%, with an F1 score, precision, and recall of 97%, 97%, and 98%, respectively, which is significantly higher compared to the existing works.

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DSI-Net: Deep Synergistic Interaction Network for Joint Classification and Segmentation With Endoscope Images

Cited by 30 publications

References 45 publications

A multi-task convolutional neural network for classification and segmentation of chronic venous disorders

A multi-task convolutional neural network for classification and segmentation of chronic venous disorders

FIAS3: Frame Importance-Assisted Sparse Subset Selection to Summarize Wireless Capsule Endoscopy Videos

A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images

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