Hossain.MD.Shakhawat
准教授教員室：A417
- 生年：
- 1987年
- 出身：
- バングラデシュ
- 所属：
- 高知工科大学情報学群
大学院工学研究科 - 所属学会：
- IEEE Society, Association of Pathology Informatics
- 学位：
- Post Doctorate
Doctor of Engineering
Masters in Engineering
Masters of Science
Bachelor of Science - 最終学歴：
- Tokyo Institute of Technology, Japan School of Engineering Department of Information and Communications Engineering 修了 (2020)
- 相談可能分野：
- Medical Image analysis, Artificial Intelligence, Computer Vision, Image Processing
- 趣味：
- Photography, Table Tennis, Traveling
Advancing Healthcare through Intelligence Image Analysis
専門分野：Medical Image Analysis, Artificial Intelligence, Digital Pathology, Whole Slide Imaging, Cancer Diagnosis
私の研究は、人工知能と画像解析技術の開発を通じて、がんの診断および予後予測の向上を目指しています。乳がん、肝臓がん、前立腺がん、卵巣がんなどのがんにおいて、診断精度の向上、治療選択の支援、治療結果の予測を目的とした多モーダル画像解析および臨床データの統合に取り組んでいます。さらに、複雑な医療画像を解析するためのスケーラブルかつ効率的な計算手法を探求しており、診断コストと診断時間の短縮に重点を置いています。これらの取り組みは、医療現場での人工知能技術の実用化と臨床応用の促進を目指しています。
原文
My research focuses on advancing cancer diagnosis and prognosis through the development of artificial intelligence and image analysis techniques. I work on multimodal image analysis and clinical data integration to enhance diagnostic accuracy, guide therapy selection, and predict treatment outcomes in cancers such as breast, liver, prostate, and ovarian. Additionally, I explore scalable and efficient computational solutions for analyzing complex medical images, with an emphasis on reducing diagnostic cost and turnaround time. These efforts aim to improve the clinical readiness and real-world applicability of AI technologies in healthcare.
私の研究は、医用画像診断における人工知能（AI）およびディープラーニングの応用に焦点を
当てており、臨床判断や患者ケアの向上を目的としています。主な研究分野は以下の通りです。
これらの研究は、診断精度の向上、モデルの汎化能力の強化、そして臨床現場でのAIシステムの
実用化を目指しています。
- Region of interest (ROI) selection using vision transformer for automatic analysis using whole slide images.
- 著者:
- Hossain, Md Shakhawat, et al.
- 発表誌：
- Scientific Reports 13.1 (2023): 11314.
- 発表年：
- 2023
- Automatic quantification of HER2 gene amplification in invasive breast cancer from chromogenic in situ hybridization whole slide images.
- 著者：
- Hossain, Md Shakhawat, et al.
- 発表誌：
- Journal of Medical Imaging 6.4 (2019): 047501-047501.
- 発表年：
- 2019
- Singular nuclei segmentation for automatic her2 quantification using cish whole slide images.
- 著者：
- Hossain, Md Shakhawat, et al.
- 発表誌：
- Sensors 22.19 (2022): 7361.
- 発表年：
- 2022
- Residual tumor cellularity assessment of breast cancer after neoadjuvant therapy using image transformer.
- 著者：
- Hossain, MD Shakhawat, et al.
- 発表誌：
- IEEE Access 12 (2024): 86083-86095.
- 発表年：
- 2024
- Predicting the effect of Bevacizumab therapy in ovarian cancer from H&E whole slide images using transformer model.
- 著者：
- Hossain, MD Shakhawat, et al.
- 発表誌：
- Intelligence-Based Medicine11 (2025): 100231.
- 発表年：
- 2025
- Automated Gleason Grading of Prostate Cancer from Low-Resolution Histopathology Images Using an Ensemble Network of CNN and Transformer Models.
- 著者：
- Hossain, MD Shakhawat, et al.
- 発表誌：
- Computers, Materials & Continua 84.2 (2025).
- 発表年：
- 2025
- Tissue artifact segmentation and severity assessment for automatic analysis using WSI.
- 著者：
- Hossain, MD Shakhawat, et al.
- 発表誌：
- IEEe Access 11 (2023): 21977-21991.
- 発表年：
- 2023
- Automatic quality evaluation of whole slide images for the practical use of whole slide imaging scanner
- 著者：
- Hossain, MD Shakhawat, et al.
- 発表誌：
- ITE Transactions On Media Technology And Applications 8.4 (2020): 252-268.
- 発表年：
- 2020
- DeepPoly: Deep learning-based polyps segmentation and classification for autonomous colonoscopy examination.
- 著者：
- Hossain, MD Shakhawat, et al.
- 発表誌：
- IEEE Access 11 (2023): 95889-95902.
- 発表年：
- 2023
医用画像解析と人工知能（AIM）研究室：A361
AIMラボは、医用画像におけるAI技術の研究を行い、コンピュータビジョンとヘルスケアの融合により診断精度や臨床判断の向上を目指しています。学際的な思考を重視し、実践的な学びや国際的な共同研究の機会を提供しています。意欲ある学生の参加を歓迎します。
原文
The AIM Lab is dedicated to advancing AI solutions in medical imaging. Our research lies at the intersection of computer vision, AI and healthcare, aiming to improve diagnostic accuracy, clinical decisions and patient outcomes. We value interdisciplinary thinking and offer a supportive environment for hands-on learning, innovation and real-world impact. Students will have opportunities to contribute to cutting-edge research, co-author publications, and collaborate with clinicians and international researchers. Interested students are encouraged to reach out and join us in transforming healthcare through intelligent solutions.
高校生のみなさんへ
原文
We welcome motivated students with interests in AI, image processing and healthcare. Knowledge of programming and image analysis is helpful. Students will have opportunities to contribute to cutting-edge research, co-author publications and engage in collaborative projects with clinicians and international researchers. We value interdisciplinary thinking and offer a supportive environment for hands-on learning, innovation and real-world impact. AIM Lab provides a supportive, innovative environment for meaningful research. Interested students are encouraged to reach out and join us in transforming healthcare through intelligent solutions.
講義科目
- Introduction to Cyber Reality
- Image Processing
- Programming with Python
- Computer Algorithms
受け入れ可能な専攻
AI・コンピュータ科学専攻
サイバーリアリティ専攻
脳情報・心理情報学専攻
Majors for International students
Computer Science and Engineering
Biomedical Engineering
Information Processing
