Topics of Interest

We invite high-quality submissions that advance the field of cancer detection and prevention through the use of advanced AI. The scope of this workshop is centered on multi-modal data and foundation models, including but not limited to the following areas:

Multi-Modal Data Integration in Cancer Care:

• Combining radiology (e.g., CT/MRI scans), pathology (e.g., histopathology slides), genomics (e.g., somatic mutations), spatial omics (e.g., transcriptomics, proteomics), and clinical data for holistic cancer analysis. 

• Techniques such as transformers for multi-modal fusion or graph neural networks (GNNs) for relational data modeling. 

• Case studies on multi-modal AI applications in cancer detection and prognosis.

•  Data processing techniques like data pipelines, cleaning, sampling/subset selection.

Multi-Modal Foundation Models for Cancer Analysis

• Architectures and training strategies for foundation models using diverse data modalities: radiology (CT, MRI), pathology (WSI), genomics, proteomics, and electronic health records (EHRs).

• Multi-modal learning techniques for fusing complementary information to improve diagnostic performance.

• Image-to-text models for automatically generating findings for radiology or pathology reports.

• Methods for longitudinal studies, tracking lesion changes and patient status over time using multi-modal history.

• Efficient voxel representations and 3D architectures for volumetric data (CT, MRI) within foundation models.

Foundation Models in Clinical Pathology: 

• Pre-trained models designed for large-scale medical imaging tasks (e.g., tumor segmentation or classification). 

• Applications in rare cancer detection, biomarker discovery, and generalization across diverse patient populations. 

• Challenges in adapting foundation models to domain-specific oncology tasks. 

AI-Driven Early Detection and Risk Prediction: 

• Leveraging AI to identify early-stage cancers through imaging biomarkers and circulating tumor DNA (ctDNA). 

• Risk stratification using genetic predispositions (e.g., BRCA mutations) and lifestyle factors. 

• Public health implications of predictive modeling for large-scale screening programs. 

Personalized Oncology: 

• Predictive modeling for therapeutic response based on tumor microenvironment characteristics by developing digital biomarkers for targeted and immunotherapy. 

• Integration of imaging-derived phenotypes with genomic alterations to guide precision oncology. 

Data-Efficient Learning and Adaptation in Oncology

• Un/semi/weakly-supervised learning for pre-training and fine-tuning on medical data where annotations are scarce or incomplete.

• Domain adaptation techniques to address performance degradation across different hospitals, imaging devices, or patient demographics (e.g., race, ethnicity).

• Active learning strategies to intelligently select the most informative data for annotation, reducing cost and maximizing model improvement for cancer-specific tasks.

• Methods for label refinement and handling noisy labels, especially for diseases with subtle visual cues or high inter-observer variability among physicians.

Robust and Interpretable AI for Clinical Translation

• Out-of-distribution (OOD) detection to ensure model safety by identifying and rejecting inputs that the model cannot reliably analyze.

• Development of explainable AI (XAI) systems tailored for oncologists and pathologists to build trust and aid in decision-making.

• Core computer vision tasks (classification, detection, segmentation) specifically benchmarked for robustness and accuracy in clinical cancer workflows.

• Image enhancement techniques (e.g., noise reduction in low-dose CT) to improve the quality and reliability of input data for diagnostic models.

AI-Driven Applications in Personalized Cancer Care

• Predictive modeling for therapeutic response based on tumor microenvironment characteristics.

• Development of novel digital biomarkers from imaging and other data for guiding targeted and immunotherapy.

• AI-powered risk stratification using genetic predispositions, imaging biomarkers, and lifestyle factors for large-scale screening programs.

Future Directions in AI-Driven Cancer Care: 

• Real-time decision-making during interventions using intra-operative imaging, pathology analysis, and any existing source of patient history. 

• Development of explainable AI systems tailored for oncologists. 

• Scaling multi-modal datasets through international collaborations to improve model robustness.