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

TL;DR

This paper introduces a deep learning framework that combines imaging, genomic, and clinical data to improve the diagnosis of blood cancers linked to mitochondrial dysfunction.

Contribution

A novel deep learning framework integrating multimodal data with attention-based fusion and adversarial adaptation for blood cancer diagnosis.

Findings

The proposed framework outperforms conventional diagnostic systems in classification accuracy.

Attention-based multimodal fusion enhances predictive accuracy and interpretability.

Adversarial domain adaptation improves robustness across diverse datasets.

Abstract

Deep 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.

To 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.

Experimental 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.

Figures