Aims and Scope
Clinical workflows are increasing data-driven. Medical data, including imaging, is routinely employed to track the progression of disease or to assess response to treatment. For example, patients with cancer are often followed up longitudinally using radiological imaging (e.g., CT/MR/PET) to identify and track lesions, and assess treatment response. Recent developments in AI and machine learning have shown promise in automating or improving parts of the clinical workflow, from being able to find lesions in various organs, to classification and diagnosis of diseases. Unfortunately, despite the key role of serial imaging in the clinical workflow, developing AI systems that can track or model disease progression by learning from and exploiting longitudinal imaging has not received much attention until recently. Besides their role in the everyday clinical workflow, such models can enhance biological understanding of diseases to shape prevention and intervention strategies, inform clinical trial design, and support clinical decision making, such as patient diagnosis and prognosis.Therefore, in this workshop, we solicit submissions which adhere to the general theme of tracking and modeling disease progression with imaging and/or multimodal data.
The following area is of particular interest: Cancer and concomitant diseases, where possible themes include tasks, such as detection, classification or segmentation of lesions or other abnormalities with or without multiple time points, combining imaging with other multi-modal data (e.g. radiology reports, electronic medical records), combining imaging and large language models (LLMs) to identify and track findings and disease status over time as well as mechanistic models.
The workshop is not limited to the above disease areas and we welcome submissions related to other diseases or applications as well. While the focus is on longitudinal data, we also welcome submissions which perform DPM with cross-sectional data. Finally, we also welcome and encourage submissions related to new datasets that can enable the research of tomorrow.
The scope of the conference includes, but is not limited to:
- Detection, classification, segmentation of abnormal anatomical structures. Areas of interest include, but are not limited to the following: lesions, tumors, lymph nodes, neurodegenerative changes etc.
- Disease progression modeling
- Longitudinal data analysis
- Follow-up assessment
- Disease tracking and outcome prediction with longitudinal and/or multi-modal data
- Surgical and Computer-Assisted interventions to determine/track changes
- Histopathological analysis and change tracking over time
- Image registration of longitudinal series for subsequent assessment
- Multi-modal data fusion for detection, diagnosis, and image-guided interventions
- New datasets and metrics
Call for Papers
We solicit submissions consistent with the aims and scope of the workshop. The following types of submissions will be accepted:
- Full Papers: We encourage the submissions of full-length papers (upto 10 pages excluding references) describing new work that has not been previously published or accepted for publication. Accepted papers will be presented as orals.
- Accepted manuscripts from MICCAI main conference: We also invite authors of an accepted manuscript at MICCAI which is of interest for our workshop to present their work in an oral presentation at our workshop to strengthen the community around this topic.
Important Dates
All deadlines are 23:59 UTC-12/Anywhere on Earth (AoE) TBAKeynote Speakers
TBAAgenda
TBAOrganizers
Clinical Center, Bethesda, USA
and Fraunhofer MEVIS, Germany
Clinical Center, Bethesda, USA