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 -- including different data analysis methods for various modalities and optimizing cross-modality fusion for improved detection and prognosis.

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 overcomed to translate computational methods to clinical practice through well designed, generalizable (robust), interpretable and clinically transferable methods. Most current methods are developed on retrospective data that do not guarantee good representation of daily clinical procedures (e.g. domain gap). We aim to identify the new ecosystem that will enable comprehensive method validation and reliability of methods, setting up a new gold standard for sample size and elaborate evaluation strategies to identify failure modes of methods when applied to real-world clinical environments.

Workshop themes

  1. Early detection and diagnosis of cancer: learning algorithms for lesion detection in medical images, staging, risk assessment, prediction of cancer outcome (in terms of life expectancy, survivability, progression, treatment sensitivity)

  2. Image-guided intervention for cancer treatment: Image fusion, multi-modal registration, detection, segmentation, and tracking, computer-guided interventions, augmented reality for tumor delineation and tumor resection.

  3. Real-world data exploration for cancer prediction: Big (imaging) data analysis, multimodal data analysis using active, semi and self-supervised learning, model-agnostic meta-learning, federated learning (sparsely labeled, data privacy), large foundational models (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. Clinically accepted evaluation methods: Identifying new evaluation metrics or gold standards (e.g., compared to widely used Dice or IoU), sample size standardization, biases, uncertainty estimation, best practices for validation, image simulation and synthesis techniques in cancer research.

Key technical themes

Medical image analysis methods targeted to the detection, diagnosis, treatment and/or monitoring of cancer that include a wide range of machine learning algorithms, workflows designed to improve patient outcome, data analysis and insights, and new imaging datasets.

Some key technical themes are listed below (not limited to):
  1. Learning algorithms & workflows (e.g., semi and self-supervised, active learning, metric learning, model-agnostic meta-learning, unsupervised learning, outlier detection, LLMs, foundational models)

  2. Data and label efficiency (e.g., limited data problem, data imbalance problem)

  3. Model robustness and generalisability (e.g., edge-AI, cloud-AI, quantized models for clinical application)

  4. Explainability, fairness and data privacy (e.g., model calibration, saliency mapping and federated learning for multi-center data)

  5. Multimodal, and multi-instance learning

Important Dates

Paper submission begins: 2nd May 2024

Submission deadline: 24th June 2024 (final extension)

Paper decision notification: 15th July 2024

Camera ready submission: 1st August 2024

Workshop day: 6th October 2024


CMT submission website TBD

Accepted papers will be published in a joint proceeding with the MICCAI 2024 conference.

All papers should be formatted according to the Lecture Notes in Computer Science templates.

We recommend submission up to 8-pages and 2-pages of references (same as MICCAI main conference) for a double-blind peer review process

In addition, since the joint workshop has adhered to the double-blinded peer review process, we ask that you please follow the MICCAI2023 anonymity guidelines when preparing your intial submission.



Accepted papers will be published in LNCS as a separate CaPTion 2024 (MICCAI Workshop), proceeding

Call for Papers

Imaging themes (not limited to):

  1. Optical imaging: Endoscopy, OCT, Hyperspectral imaging, opto-acoustics

  2. CT/PET fusion, MRI

  3. New imaging biomarkers

  4. Multimodal imaging

  5. Ultrasound

Clinical applications (not limited to):

  1. Early cancer detection and diagnosis

  2. Prognosis and prediction

  3. Tumor characterisation, cancer staging

  4. Longitudinal patient studies

  5. Surgical data science

  6. Digital histopathology

  7. Phenotypic tumor correlation

Organising committee

Sharib Ali Fons van der Sommen Noha Ghatwary
Sharib Ali
University of Leeds, UK
Fons van der Sommen
TU/e, Eindhoven, The Netherlands
Noha Ghatwary
Bartek Papiez Yueming Jin Iris Kolenbrander
Bartek Papiez
University of Oxford, UK
Yueming Jin
National University of Singapore
Iris Kolenbrander
TU/e, Eindhoven, The Netherlands

Student Representatives

Raneem Toman
University of Leeds, UK
Pedro Chavarrias Solano
University of Leeds, UK


Please contact us for more details or if you are interested in sponsoring the event. Contact CaPTion team at: or: Sharib Ali

Contact us

Please contact us for more details or if you are interested in sponsoring the event. Contact CaPTion team at:

Previous workshops

CaPTion2022 Springer Proceeding

CaPTion2023 Springer Proceeding