CaPTion Workshop @ MICCAI 2026

Workshop Description

While computational methods in medical imaging have enabled us to detect and assess cancerous tumors and assist in their treatment, early detection of cancer precursors provides us with an opportunity for its early treatment and prevention. The survival rate of cancer is still low, and largely depends on the affected organ and how early it is diagnosed.

The variable nature of the disease in different patients and the diverse imaging acquisition types involved for quantification of disease and treatment demands robust method designs. It is therefore critical to develop generalizable methods as part of a holistic early cancer detection ecosystem.

The workshop will invite researchers in the field of medical imaging around the central theme of data-driven cancer detection and treatment, and strives to address the challenges that are required to be overcome to translate computational methods to clinical practice through well designed, generalizable (robust), interpretable and clinically transferable methods.

New this year: We will be including an exciting panel discussion on "Tackling medical imaging challenges in early detection, prevention and treatment of cancer."

Workshop Themes

1. Early Detection & Diagnosis

Learning algorithms for lesion detection in medical images, staging, risk assessment, prediction of cancer outcome.

2. Image-Guided Intervention

Image fusion, multi-modal registration, detection, segmentation, and tracking, computer-guided interventions, augmented reality.

3. Real-World Data Exploration

Big imaging data analysis, active, semi & self-supervised learning, meta-learning, federated learning, LLMs, and continual learning.

4. Cancer Biomarkers

New predictive visual biomarker discovery in medical images, tumor data signatures, personalized cancer treatments, genomics and radiomics.

5. Clinical Evaluation Methods

Identifying new evaluation metrics or gold standards, sample size standardization, biases, uncertainty estimation, and image simulation techniques.

Key Technical Themes

Learning algorithms & workflows (e.g., self-supervised, active learning, LLMs)
Data and label efficiency (limited data, imbalance)
Model robustness and generalisability (edge-AI, quantized models)
Explainability, fairness and data privacy (federated learning)
Multimodal and multi-instance learning

Call for Papers (Applications)

Imaging Modalities: Optical, Endoscopy, OCT, Hyperspectral, CT/PET fusion, MRI, Ultrasound.
Clinical Apps: Early cancer detection, prognosis, tumor characterization, staging, longitudinal studies.
Domains: Surgical data science, digital histopathology, phenotypic tumor correlation.

Submission Guidelines