Explainable artificial intelligence for multi-modal cancer analysis: from genomics to immunology

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Deep learning for healthcare: review, opportunities and challenges

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Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions

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Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data

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Efficient integration of heterogeneous single-cell transcriptomes using Scanorama

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Artificial intelligence for multimodal data integration in oncology

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Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis

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Deep learning in cancer pathology: a new generation of clinical biomarkers

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Harnessing multimodal data integration to advance precision oncology

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Pan-cancer integrative histology-genomic analysis via multimodal deep learning

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Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

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Multimodal mapping of the tumor and peripheral blood immune landscape in human pancreatic cancer

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Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent

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Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images

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The Application of Deep Learning in Cancer Prognosis Prediction

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Intratumoral heterogeneity and clonal evolution in liver cancer

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Artificial Intelligence for Predictive Biomarker Discovery in Immuno-Oncology: A Systematic Review

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From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and Opportunities

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FDA Perspective on the Regulation of Artificial Intelligence in Health Care and Biomedicine

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Heterogeneity of the tumor immune microenvironment and its clinical relevance

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How Explainable Artificial Intelligence Can Increase or Decrease Clinicians’ Trust in AI Applications in Health Care: Systematic Review

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Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice

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A Review of Deep Learning-Based Information Fusion Techniques for Multimodal Medical Image Classification

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Benchmarking saliency methods for chest X-ray interpretation

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Convolutional neural network models for cancer type prediction based on gene expression

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A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches

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Federated learning improves site performance in multicenter deep learning without data sharing

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Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review

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Early, intermediate and late fusion strategies for robust deep learning-based multimodal action recognition

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Immunotherapy and the ovarian cancer microenvironment: Exploring potential strategies for enhanced treatment efficacy

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Human-centered XAI: Developing design patterns for explanations of clinical decision support systems

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Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator

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Tumor heterogeneity: preclinical models, emerging technologies, and future applications

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In-depth tissue profiling using multiplexed immunohistochemical consecutive staining on single slide

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Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma

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Artificial intelligence in oncology: path to implementation

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Unmasking bias in artificial intelligence: A systematic review of bias detection and mitigation strategies in electronic health record-based models

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SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism

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10-year performance of four models of breast cancer risk: a validation study

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Multimodal data integration for oncology in the era of deep neural networks: a review

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Harmonization strategies for multicenter radiomics investigations

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Deep Learning in Proteomics

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A transformer-based weakly supervised computational pathology method for clinical-grade diagnosis and molecular marker discovery of gliomas

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Next-generation computational tools for interrogating cancer immunity

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Vision Transformers for Computational Histopathology

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A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging

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A comprehensive investigation of multimodal deep learning fusion strategies for breast cancer classification

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Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology

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Deep convolutional neural networks for accurate somatic mutation detection

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Deep learning meets metabolomics: a methodological perspective

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Recent Advances in Molecular Mechanisms of Cancer Immunotherapy

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Survey and perspective on verification, validation, and uncertainty quantification of digital twins for precision medicine

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Computational Prediction and Validation of Tumor-Associated Neoantigens

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On the importance of interpretable machine learning predictions to inform clinical decision making in oncology

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Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method

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Multimodal Deep Learning Approaches for Precision Oncology: A Comprehensive Review

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Biology-guided deep learning predicts prognosis and cancer immunotherapy response

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Multimodal deep learning to predict prognosis in adult and pediatric brain tumors

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Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity

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Generalized ComBat harmonization methods for radiomic features with multi-modal distributions and multiple batch effects

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The Benefits and Safety of Monoclonal Antibodies: Implications for Cancer Immunotherapy

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Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples

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Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas

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From modern CNNs to vision transformers: Assessing the performance, robustness, and classification strategies of deep learning models in histopathology

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Deep learning supported discovery of biomarkers for clinical prognosis of liver cancer

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A systematic review of intermediate fusion in multimodal deep learning for biomedical applications

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Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging

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Explainable Artificial Intelligence (XAI) in healthcare: interpretable models for clinical decision support

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Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

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Explainable deep learning in healthcare: A methodological survey from an attribution view

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A systematic review on the use of explainability in deep learning systems for computer aided diagnosis in radiology: Limited use of explainable AI?

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Deep learning prediction models based on EHR trajectories: A systematic review

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All you need is data preparation: A systematic review of image harmonization techniques in Multi-center/device studies for medical support systems

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OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM

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Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review

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Translation of AI into oncology clinical practice

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The use of Gene Ontology terms and KEGG pathways for analysis and prediction of oncogenes

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Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model

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Harmonization Strategies in Multicenter MRI-Based Radiomics

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Machine-Learning-Based Late Fusion on Multi-Omics and Multi-Scale Data for Non-Small-Cell Lung Cancer Diagnosis

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NIMG-68. FEDERATED LEARNING IN NEURO-ONCOLOGY FOR MULTI-INSTITUTIONAL COLLABORATIONS WITHOUT SHARING PATIENT DATA

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Bayesian network enables interpretable and state-of-the-art prediction of immunotherapy responses in cancer patients

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Immuno-Oncology at the Crossroads: Confronting Challenges in the Quest for Effective Cancer Therapies

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FUSION: a web-based application for in-depth exploration of multi-omics data with brightfield histology

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Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification

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Fed-ComBat: A Generalized Federated Framework for Batch Effect Harmonization in Collaborative StudiesPreprint

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A multi-task deep learning model based on comprehensive feature integration and self-attention mechanism for predicting response to anti-PD1/PD-L1

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Navigating the Multiverse: a Hitchhiker’s guide to selecting harmonization methods for multimodal biomedical data

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Disentangled and Interpretable Multimodal Attention Fusion for Cancer Survival Prediction

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A Comprehensive, Artificial Intelligence, Digital Twin Platform Based on Multimodal Real-World Data Integration for Personalized Medicine in Hematology

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SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data

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Proposal for a New Classification of Rare Cancers Adopting Updated Histological Tumor Types

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Interpretable AI in Medical Imaging: Enhancing Diagnostic Accuracy through Human-Computer Interaction

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Translating the machine; An assessment of clinician understanding of ophthalmological artificial intelligence outputs

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Revealing the Impact of Genomic Alterations on Cancer Cell Signaling with an Interpretable Deep Learning Model

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Exploring Cancer Genomics with Graph Convolutional Networks: A Comparative Explainability Study with Integrated Gradients and SHAP

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AI generated annotations for Breast, Brain, Liver, Lungs, and Prostate cancer collections in the National Cancer Institute Imaging Data Commons

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Mechanistically Explainable AI Model for Predicting Synergistic Cancer Therapy Combinations

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PathX-CNN: An Enhanced Explainable Convolutional Neural Network for Survival Prediction and Pathway Analysis in GlioblastomaPreprint

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Editorial: Unveiling biomarkers and mechanisms in the tumor-immune nexus

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Gene expression-based machine learning approach to predict immunotherapy response and survival in patients with advanced cancer.

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Abstract 888: Uncovering clinically relevant omics signatures from pan-cancer imaging and multi-omics data integration

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Using Causal Inference to Solve Uncertainty Issues in Dataset Shift

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Abstract 6202: Integrating public single-cell transcriptomics and patient profiles to guide clinical development

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Can Causal Thinking Render AI-Driven Systems More Robust?Preprint

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Pathology-Genomic Cross Attention network for Cancer Survival Prediction from Whole Slide Image

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Advances in Multi-Modal Biomarkers for Immunotherapy Response in Non-Small Cell Lung Cancer: ctDNA, Microbiome, and Radiomics

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Digital pathology and artificial intelligence in breast and gynecologic oncology: from molecular prediction to multimodal integration

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A generative AI multi-agent framework with integrated XAI governance for cancer diagnostics: from multi-omics interpretation to lifestyle risk stratification

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Causal AI for cancer immunotherapy: a narrative framework review of target trial emulation, treatment-effect learning and clinical translation

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Perineural invasion in solid tumors: biological foundations and the emerging integration of machine learning and artificial intelligence

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Explainable attention-based multi-omics fusion with protein language models for CML-versus-control classification and biomarker discovery

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Restricted Multimodal Oncology AI: Role-Aware Evidence Organisation and Cross-Domain Transfer BoundariesPreprint

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Leveraging traditional and advanced machine learning for comprehensive oncology data integration and outcome prediction

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EXPLAINABLE MULTIMODAL ARTIFICIAL INTELLIGENCE IN PRECISION ONCOLOGY: FROM BIOMARKER DISCOVERY TO CLINICAL ADOPTION

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AI-assisted multimodal data integration for precision oncology

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A Cross-Modal Attention Framework for Robust Cancer Detection

Saleha, I., & Kelwade, P. K. (2026) · International Journal of Drug Delivery Technology · doi:10.25258/ijddt.16.33s.61

Artificial intelligence for multimodal data integration in oncology

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Multimodal CustOmics: A unified and interpretable multi-task deep learning framework for multimodal integrative data analysis in oncology

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