Call For Contributions
AI4Treat - AI for Treatment Response Assessment and predicTion
October 2023 - Vancouver, Canada
We would like to invite you to submit your papers for AI4Treat 2023 workshop to be held in October, 2023 in Vancouver, Canada, as a satellite workshop of MICCAI and organized with the contribution of the European Society of Radiology (ESR).
Paper submission
AI4Treat welcomes contributions from the following areas:
- Radiomics approaches for treatment response prediction
- Deep Learning approaches for treatment response prediction
- Combining Multi-Omics and clinical imaging for treatment response prediction
- Machine Learning for treatment response assessment
- Multi-center generalizability of treatment response prediction
- Explainable AI in treatment response prediction and assessment
- Causal learning in treatment response prediction
- Clinical perspective on including AI predictive models in the radiological workflow
- Prospective validation of machine learning models in treatment response prediction
The conference program will include paper presentations, presentation of accepted contributions and keynote talks by prominent speakers in the field. All submissions will be reviewed by 3 reviewers. The selection of the papers will be based on their relevance for medical image analysis, significance of results, technical and experimental merit, and clear presentation.
Abstract submission
We welcome the submission of abstracts from different disciplines, both clinical and scientific. The aim is to foster a vibrant discussion and build bridges between both worlds. These abstracts, up to one page in length (excluding references), are intended for oral presentation. We welcome completed or preliminary research on topics listed below but also on perspectives of AI in clinical practice.
- Radiomics approaches for cancer treatment response prediction
- Deep Learning approaches for cancer treatment response prediction
- Combining Multi-Omics and clinical imaging for cancer treatment response prediction
- Machine Learning for cancer treatment response assessment
- Multi-centre generalizability of cancer treatment response prediction
- Explainable AI in cancer treatment response prediction and assessment
- Causal learning in cancer treatment response prediction
- Prospective validation of machine learning models in cancer treatment response prediction
- Clinical perspective on translating AI research results into clinical practice
- Clinical perspective on including AI in the Radiological departmental workflow
- Clinical perspective on including AI in the Radiologist’s diagnostic workflow
For more information about abstract submission, visit the submission page. Don’t miss this opportunity!
Looking forward to your submissions,
The organizing committee