期刊名：Bioengineering

期刊主页：https://www.mdpi.com/journal/bioengineering

人工智能与先进成像技术的深度融合正推动肿瘤精准诊疗进入新阶段。本专题汇集发表于 Bioengineering 的系列研究，系统展示了多模态成像（CT、MRI、热成像、高光谱成像、OCT）与先进人工智能算法（混合量子架构、视觉Transformer、可解释AI、自动化机器学习）在多种癌症检测中的创新应用。研究覆盖了肺癌（基于CT和放射影像的自动检测）、乳腺癌（高光谱成像、热成像、计算机辅助诊断）、宫颈癌（轻量化Transformer框架）、皮肤癌（高光谱与机器学习）、食管癌（精准成像）、前列腺癌（放射组学与自动化框架）及脑肿瘤（无监督聚类与深度学习）等关键领域。这些工作共同展示了人工智能如何赋能多源影像数据，推动癌症早期检测、精准分型与临床决策的智能化发展。

1. 基于混合量子结构的肺癌检测新方法

Novel Hybrid Quantum Architecture-Based Lung Cancer Detection Using Chest Radiograph and Computerized Tomography Images

https://www.mdpi.com/2306-5354/11/8/799

Martis, J.E.; M S, S.; R, B.; Mutawa, A.M.; Murugappan, M. Novel Hybrid Quantum Architecture-Based Lung Cancer Detection Using Chest Radiograph and Computerized Tomography Images. Bioengineering 2024, 11, 799.

2. Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging

计算机辅助高光谱成像检测乳腺癌的系统性Meta分析

https://www.mdpi.com/2306-5354/11/11/1060

Leung, J.-H.; Karmakar, R.; Mukundan, A.; Thongsit, P.; Chen, M.-M.; Chang, W.-Y.; Wang, H.-C. Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging. Bioengineering 2024, 11, 1060.

3. Hierarchical Swin Transformer Ensemble with Explainable AI for Robust and Decentralized Breast Cancer Diagnosis

基于可解释AI的分层Swin Transformer Ensemble用于稳健和分散的乳腺癌诊断

https://www.mdpi.com/2306-5354/12/6/651

Ahmed, M.R.; Rahman, H.; Limon, Z.H.; Siddiqui, M.I.H.; Khan, M.A.; Pranta, A.S.U.K.; Haque, R.; Swapno, S.M.M.R.; Cho, Y.-I.; Abdallah, M.S. Hierarchical Swin Transformer Ensemble with Explainable AI for Robust and Decentralized Breast Cancer Diagnosis. Bioengineering 2025, 12, 651.

4. Lightweight Low-Rank Adaptation Vision Transformer Framework for Cervical Cancer Detection and Cervix Type Classification

用于宫颈癌检测和宫颈类型分类的轻量化低秩自适应视觉Transformer框架

https://www.mdpi.com/2306-5354/11/5/468

Hong, Z.; Xiong, J.; Yang, H.; Mo, Y.K. Lightweight Low-Rank Adaptation Vision Transformer Framework for Cervical Cancer Detection and Cervix Type Classification. Bioengineering 2024, 11, 468.

5. OCT in Oncology and Precision Medicine: From Nanoparticles to Advanced Technologies and AI

OCT在肿瘤学和精准医学中的应用：从纳米颗粒到先进技术和AI

https://www.mdpi.com/2306-5354/12/6/650

Daneshpour Moghadam, S.; Maris, B.; Mokhtari, A.; Daffara, C.; Fiorini, P. OCT in Oncology and Precision Medicine: From Nanoparticles to Advanced Technologies and AI. Bioengineering 2025, 12, 650.

6. Hyperspectral Imaging for Enhanced Skin Cancer Classification Using Machine Learning

使用机器学习的高光谱成像增强皮肤癌分类

https://www.mdpi.com/2306-5354/12/7/755

Lin, T.-L.; Mukundan, A.; Karmakar, R.; Avala, P.; Chang, W.-Y.; Wang, H.-C. Hyperspectral Imaging for Enhanced Skin Cancer Classification Using Machine Learning. Bioengineering 2025, 12, 755.

7. Precision Imaging for Early Detection of Esophageal Cancer

食管癌早期诊断的精确成像

https://www.mdpi.com/2306-5354/12/1/90

Yang, P.-C.; Huang, C.-W.; Karmakar, R.; Mukundan, A.; Chen, T.-H.; Chou, C.-K.; Yang, K.-Y.; Wang, H.-C. Precision Imaging for Early Detection of Esophageal Cancer. Bioengineering 2025, 12, 90.

8. Breast Cancer Detection Using Infrared Thermography: A Survey of Texture Analysis and Machine Learning Approaches

使用红外热成像检测乳腺癌：纹理分析和机器学习方法综述

https://www.mdpi.com/2306-5354/12/6/639

Ryan, L.; Agaian, S. Breast Cancer Detection Using Infrared Thermography: A Survey of Texture Analysis and Machine Learning Approaches. Bioengineering 2025, 12, 639.

9. An Innovative Thermal Imaging Prototype for Precise Breast Cancer Detection: Integrating Compression Techniques and Classification Methods

用于精确乳腺癌检测的创新热成像原型：集成压缩技术和分类方法

https://www.mdpi.com/2306-5354/11/8/764

Ahmed, K.S.; Sherif, F.F.; Abdallah, M.S.; Cho, Y.-I.; ElMetwally, S.M. An Innovative Thermal Imaging Prototype for Precise Breast Cancer Detection: Integrating Compression Techniques and Classification Methods. Bioengineering 2024, 11, 764.

10. An Automated Diagnosis Method for Lung Cancer Target Detection and Subtype Classification-Based CT Scans

一种基于CT扫描的肺癌自动诊断方法

https://www.mdpi.com/2306-5354/11/8/767

Wang, L.; Zhang, C.; Zhang, Y.; Li, J. An Automated Diagnosis Method for Lung Cancer Target Detection and Subtype Classification-Based CT Scans. Bioengineering 2024, 11, 767.

11. Study of a Deep Convolution Network with Enhanced Region Proposal Network in the Detection of Cancerous Lung Tumors

基于增强区域建议网络的深度卷积网络在肺癌检测中的研究

https://www.mdpi.com/2306-5354/11/5/511

Lee, J.-D.; Hsu, Y.-T.; Chien, J.-C. Study of a Deep Convolution Network with Enhanced Region Proposal Network in the Detection of Cancerous Lung Tumors. Bioengineering 2024, 11, 511.

12. Simplatab: An Automated Machine Learning Framework for Radiomics-Based Bi-Parametric MRI Detection of Clinically Significant Prostate Cancer

Simplatab：一种用于临床显著前列腺癌放射学双参数MRI检测的自动化机器学习框架

https://www.mdpi.com/2306-5354/12/3/242

Zaridis, D.I.; Pezoulas, V.C.; Mylona, E.; Kalantzopoulos, C.N.; Tachos, N.S.; Tsiknakis, N.; Matsopoulos, G.K.; Regge, D.; Papanikolaou, N.; Tsiknakis, M.; et al. Simplatab: An Automated Machine Learning Framework for Radiomics-Based Bi-Parametric MRI Detection of Clinically Significant Prostate Cancer. Bioengineering 2025, 12, 242.

13. ViT-PSO-SVM: Cervical Cancer Predication Based on Integrating Vision Transformer with Particle Swarm Optimization and Support Vector Machine

基于视觉Transformer、粒子群优化和支持向量机的宫颈癌预测

https://www.mdpi.com/2306-5354/11/7/729

AlMohimeed, A.; Shehata, M.; El-Rashidy, N.; Mostafa, S.; Samy Talaat, A.; Saleh, H. ViT-PSO-SVM: Cervical Cancer Predication Based on Integrating Vision Transformer with Particle Swarm Optimization and Support Vector Machine. Bioengineering 2024, 11, 729.

14. Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments

使用人工智能方法提高医疗保健环境中自动脑肿瘤检测的准确性

https://www.mdpi.com/2306-5354/11/6/627

Abdusalomov, A.; Rakhimov, M.; Karimberdiyev, J.; Belalova, G.; Cho, Y.I. Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments. Bioengineering 2024, 11, 627.

15. Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier

基于改进无监督聚类和机器学习分类器的脑肿瘤检测与分类

https://www.mdpi.com/2306-5354/11/3/266

Bhimavarapu, U.; Chintalapudi, N.; Battineni, G. Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier. Bioengineering 2024, 11, 266.

Bioengineering 期刊介绍

主编：Anthony Guiseppi-Elie, Anderson University, USA

主要发表生物医学工程及应用，生物过程和生物系统工程与应用，生物分子、细胞和组织工程及应用，以及生化工程与应用等相关领域的最新科学技术及应用。期刊已被PubMed、Scopus、SCIE (Web of Science) 等数据库收录。