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Bioengineering
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Advanced Technique for Endoscopic Diagnosis in Biomedical Engineering
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Advanced Technique for Endoscopic Diagnosis in Biomedical Engineering

A Special Issue of Bioengineering (ISSN 2306-5354) belonging to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1680

Editor

Dr. Changseok Bang

E-Mail Website
Guest Editor
Institute for Liver and Digestive Diseases, Hallym University, Chuncheon 24252, Republic of Korea
Interests: helicobacter pylori; meta-analysis; medicine; endoscopy; gastroenterology; gastric cancer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue explores new techniques for endoscopic diagnosis, which promise to revolutionize the field of gastroenterology. Endoscopy is a critical tool in the diagnosis and treatment of various gastrointestinal disorders, but current techniques are limited by factors such as accuracy, invasiveness, and patient discomfort. This Special Issue aims to propose novel approaches to overcome these limitations by providing more accurate, less invasive, and patient-friendly methods for diagnosing gastrointestinal conditions.

We also seek to explore broader diagnostic challenges. The field of medicine is undergoing a profound transformation driven by artificial intelligence. This Special Issue aims to capture the forefront of this revolution by compiling high-quality research on the development, validation, and clinical integration of artificial intelligence systems for gastrointestinal medicine.

By utilizing state-of-the-art technologies and methodologies, there is immense potential to improve patient outcomes and advance the field of gastrointestinal medicine.

Dr. Changseok Bang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • endoscopic diagnosis
  • gastroenterology
  • novel technique
  • precision medicine
  • minimally invasive
  • patient- friendly
  • state-of-the-art technology
  • gastrointestinal disorders
  • diagnostic accuracy
  • artificial intelligence
  • research innovation

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (2 papers)

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Research

20 pages, 1227 KB  
Article
Cross-Domain Robust Pruning for Polyp Segmentation: Multi-Encoder Feature Fusion Beats Single-Encoder Baselines
by Chia-Pei Tang, Hong-Yi Chang, Tzu-Shan Chang, Yu-Chieh Chang and Chia-Hsin Cheng
Bioengineering 2026, 13(7), 759; https://doi.org/10.3390/bioengineering13070759 - 29 Jun 2026
Viewed by 574
Abstract
Medical image segmentation requires dense pixel-level annotations, making large-scale dataset construction expensive and motivating research into data-efficient training. The main objective of this paper is to determine whether fusing two complementary pretrained image encoders into a single similarity space can make training-free dataset [...] Read more.
Medical image segmentation requires dense pixel-level annotations, making large-scale dataset construction expensive and motivating research into data-efficient training. The main objective of this paper is to determine whether fusing two complementary pretrained image encoders into a single similarity space can make training-free dataset pruning robust across heterogeneous polyp segmentation domains and to quantify that robustness against a comprehensive panel of baselines. To achieve this, we propose Multi-Encoder Diverse Pruning (MEDP), a training-free dataset-pruning method. MEDP fuses features from an ImageNet-pretrained ResNet-18 and a self-supervised DINOv2 ViT-S/14 into a single 896-D similarity space. It partitions the training pool via Louvain modularity maximization and selects per-community samples via maximal-marginal-relevance (MMR) ranking, which effectively balances eigenvector centrality with feature-space diversity. We benchmarked MEDP against 12 baselines at a 20% retention ratio across three polyp segmentation settings (Kvasir-SEG, CVC-ClinicDB, and a Combined cross-domain pool) using a standard 5-level UNet. Based on approximately 330 controlled training runs, the results demonstrate that MEDP achieves the highest mean test Dice of 0.7324 on the most challenging Combined cross-domain pool, significantly outperforming uniform random sampling (Cohen’s d = +1.79, paired Wilcoxon p = 0.002). Conversely, all hand-crafted structure-aware variants failed to outperform uniform random sampling. These findings confirm that combining multi-encoder features with MMR diversity provides a simple and effective strategy for improving robustness across heterogeneous medical imaging settings and that the choice of pretrained image encoder is the dominant factor in segmentation-aware pruning. Full article
(This article belongs to the Special Issue Advanced Technique for Endoscopic Diagnosis in Biomedical Engineering)
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31 pages, 4502 KB  
Article
A Unified Framework for Classification and Segmentation of Ambiguous Dual-Type Lesions in Colonoscopic Images
by Siqi Chen, Kun Jiang, Ruishi Lin, Xiufeng Su and Liyong Ma
Bioengineering 2026, 13(6), 679; https://doi.org/10.3390/bioengineering13060679 - 11 Jun 2026
Viewed by 660
Abstract
Accurate analysis of lesions in colonoscopic images is essential for computer-aided diagnosis. However, most existing methods are designed for single-lesion segmentation and assume a predefined lesion category, limiting their applicability in real-world scenarios where multiple lesion types exhibit similar visual characteristics. To address [...] Read more.
Accurate analysis of lesions in colonoscopic images is essential for computer-aided diagnosis. However, most existing methods are designed for single-lesion segmentation and assume a predefined lesion category, limiting their applicability in real-world scenarios where multiple lesion types exhibit similar visual characteristics. To address this issue, we propose a unified framework for the joint classification and segmentation of dual-type lesions in colonoscopic images, enabling simultaneous identification and localization of submucosal lesions and polyps/adenomas. The proposed method integrates joint supervision, context-aware feature enhancement, and ambiguity-aware optimization to improve consistency between semantic recognition and spatial delineation. In particular, a soft-label supervision strategy is introduced to alleviate semantic ambiguity, while an imbalance-aware loss design enhances segmentation accuracy and reduces false negative predictions. Extensive experiments on both private and public datasets demonstrate that the proposed method achieves superior performance compared with representative CNN- and transformer-based approaches. Notably, the method shows clear advantages in segmentation accuracy, localization precision, and robustness under challenging conditions. Ablation studies further confirm the effectiveness of each component in the proposed framework. These results indicate that the proposed approach provides an effective solution for dual-type lesion analysis and has the potential to assist clinical decision-making in gastrointestinal endoscopy. Full article
(This article belongs to the Special Issue Advanced Technique for Endoscopic Diagnosis in Biomedical Engineering)
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