ArticleSensors (Basel, Switzerland)2021
Multi-Task Model for Esophageal Lesion Analysis Using Endoscopic Images: Classification with Image Retrieval and Segmentation with Attention.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 26 citations in OpenAlex.
- A Unified Framework for Classification and Segmentation of Ambiguous Dual-Type Lesions in Colonoscopic Images.Bioengineering (Basel, Switzerland) · 2026Article
- Multimodal-multiscale hybrid fusion network for automated esophageal cancer diagnosis.iScience · 2026Article
- A YOLO Ensemble Framework for Detection of Barrett's Esophagus Lesions in Endoscopic Images.Diagnostics (Basel, Switzerland) · 2025Article
- A Review of Application of Deep Learning in Endoscopic Image Processing.Journal of imaging · 2024Review
- Preparing Well for Esophageal Endoscopic Detection Using a Hybrid Model and Transfer Learning.Cancers · 2023Article
- Endoscopic Image Classification Based on Explainable Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- An Improved Crucible Spatial Bubble Detection Based on YOLOv5 Fusion Target Tracking.Sensors (Basel, Switzerland) · 2022Article
- Deep Feature Fusion and Optimization-Based Approach for Stomach Disease Classification.Sensors (Basel, Switzerland) · 2022Article
- Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model.Computational intelligence and neuroscience · 2022Article
- Image Retrieval Using Different Distance Methods and Color Difference Histogram Descriptor for Human Healthcare.Journal of healthcare engineering · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
The automatic analysis of endoscopic images to assist endoscopists in accurately identifying the types and locations of esophageal lesions remains a challenge. In this paper, we propose a novel multi-task deep learning model for automatic diagnosis, which does not simply replace the role of endoscopists in decision making, because endoscopists are expected to correct the false results predicted by the diagnosis system if more supporting information is provided. In order to help endoscopists improve the diagnosis accuracy in identifying the types of lesions, an image retrieval module is added in the classification task to provide an additional confidence level of the predicted types of esophageal lesions. In addition, a mutual attention module is added in the segmentation task to improve its performance in determining the locations of esophageal lesions. The proposed model is evaluated and compared with other deep learning models using a dataset of 1003 endoscopic images, including 290 esophageal cancer, 473 esophagitis, and 240 normal. The experimental results show the promising performance of our model with a high accuracy of 96.76% for the classification and a Dice coefficient of 82.47% for the segmentation. Consequently, the proposed multi-task deep learning model can be an effective tool to help endoscopists in judging esophageal lesions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.