AUTHOR=Zhu Feng , Liu Zihan , Chang Jianming , Qin Yuanyuan , Wang Lulu 

TITLE=Deep learning for scene understanding in mitochondrial dysregulation and blood cancer diagnosis

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1609851

DOI=10.3389/fonc.2025.1609851

ISSN=2234-943X

ABSTRACT=IntroductionDeep learning has emerged as a transformative tool in biomedical research, particularly in understanding disease mechanisms and enhancing diagnostic precision. Mitochondrial dysfunction has been increasingly recognized as a critical factor in hematological malignancies, necessitating advanced computational models to extract meaningful insights from complex biological and clinical data. Traditional diagnostic approaches rely heavily on histopathological examination and molecular profiling, yet they often suffer from subjectivity, limited scalability, and challenges in integrating multimodal data sources.MethodsTo address these limitations, we propose a novel deep learning framework that integrates medical imaging, genomic information, and clinical parameters for comprehensive scene understanding in mitochondrial dysregulation-related blood cancers. Our methodology combines self supervised learning, vision transformers, and graph neural networks to extract and fuse modality-specific features. The model architecture includes dedicated encoders for visual, genomic, and clinical data, which are integrated using an attention-based multimodal fusion mechanism. Adversarial domain adaptation and uncertainty quantification modules are incorporated to enhance generalizability and decision reliability. Our model employs a multimodal fusion strategy with attention-based learning mechanisms to enhance predictive accuracy and interpretability. Adversarial domain adaptation ensures robustness across heterogeneous datasets, while uncertainty quantification techniques provide reliable decision support for personalized treatment strategies.Results and discussionExperimental results demonstrate significant improvements in classification performance, with our approach outperforming conventional machine learning and rule-based diagnostic systems. By leveraging deep learning for enhanced scene understanding, this work contributes to a more precise and scalable framework for the early detection and management of blood cancers.