Recently, Deep Learning has made rapid advances in the performance of medical image analysis challenging physicians in their traditional fields. In the pathology and radiology fields, in particular, automated procedures can help to reduce the workload of pathologists and radiologists and increase the accuracy and precision of medical image assessment, which is often considered subjective and not optimally reproducible. In addition, Deep Learning and Computer Vision demonstrate the ability/potential to extract more clinically relevant information from medical images than what is possible in current routine clinical practice by human assessors. Nevertheless, considerable development and validation work lie ahead before AI-based methods can be fully ready for integrated into medical departments.
The workshop on AI-enabled medical image analysis (AIMIA) at ECCV 2022 aims to foster discussion and presentation of ideas to tackle the challenges of whole slide image and CT/MRI/X-ray analysis/processing and identify research opportunities in the context of Digital Pathology and Radiology/COVID19.
High-quality original contributions should be targeted in several contexts such as, using self-supervised and unsupervised methods to enforce shared patterns emerging directly from data, developing strategies to leverage few (or partial) annotations, promoting interpretability in both model development and/or the results obtained, or ensuring generalizability to support medical staff in their analysis of data coming from multi-centres, multi-modalities or multi-diseases.
The AIMIA workshop has the objective to provide a platform for scientific discussion on medical image analysis/processing, introducing the challenges of Whole-Slide Images and CT/MRI/X-ray images to the Computer Vision and Artificial Intelligence community.
The AIMIA workshop welcomes works that focus on (but not limited to):
Semi-/weakly-/self-/supervised learning methodologies;
Detection, classification and segmentation;
Disease diagnosis, grading and prognosis;
Treatment response prediction;
Detection of biomarkers with predictive/prognostic value;
applied to Digital Pathology (TRACK A) and Radiology/COVID19 (TRACK B) images.
The workshop also invites submissions to the 2nd COV19D Competition, organised within TRACK B. The top-3 performing teams are expected to contribute with a paper describing their approach, methodology and results. All the other teams are also invited to submit their solutions and final results.
Authors should prepare a manuscript of no more than 14 pages, including images and tables (with possible extra pages containing only cited references). The manuscript submitted to the workshop should be formatted according to the ECCV 2022 style and anonymised. Papers that are not properly anonymised, or do not use the template, or have more than 14 pages (excluding references) will be desk-rejected.
Authors can also submit supplementary material and code, provided it is in pdf or zip format (only), anonymised and properly referenced in the paper. Please refer to the ECCV 2022 submission guidelines for more details.
All submissions will be evaluated by 3 reviewers, in a double-blind reviewing process. Authors/reviewers will be asked to disclose any potential conflicts of interest, such as collaborations in the last 3 years. The selection of the papers will be based on their relevance for the workshop topics, technical and experimental quality, significance of results, and clear presentation.
The workshop accepted papers will be published in Springer as part of the ECCV 2022 proceedings (workshops set).
Submission deadline: July 08, 2022*
Author notification: August 05, 2022*
Camera ready deadline: August 12, 2022*
AIMIA workshop: October 2022 (TBD)
*All deadlines are at 11:59AM PST