As the drive towards precision medicine has accelerated, the opportunities
and challenges in using computational approaches in cancer research and
clinical application are rapidly growing. The rapid rise of deep learning as an
enabling technology and its potential are reshaping the way computation is
being applied across scales scales of computing, across time and across spatial
scales. With recent legislation in the form of the Twenty-first Century Cures
Act as well as efforts of the Beau Biden Cancer Moonshot all underscore the
importance of a workshop that brings together experts and insights across the
spectrum of computational approaches for cancer.
In the workshop, we bring together the computational community exploring and using high-performance computing, analytics, predictive modeling, and large datasets in cancer research and clinical applications. The workshop is inherently inter-disciplinary, with the common interest in cancer and computation the unifying theme. As such, the workshop provides rich opportunities for attendees to learn about future directions, current applications and challenges and build collaborations. Maintaining a perspective of accelerating scientific insights and translation of insights to clinical application for improved patient outcomes, the workshop brings together many interests from across the technology, cancer research and clinical domains.
The CAFCW workshop annually identifies a special workshop focus of
significant interest to the community, bringing a special emphasis to the
workshop for the year.
The use of machine learning in multiple contexts (AI, cognitive learning, deep learning, etc.) has dramatically accelerated in the cancer research and clinical space. This has led to several innovations and rapid development of new techniques, while highlighting key challenges to overcome in order to more fully utilize these technologies. Papers are sought for a workshop session emphasizing cancer applications of machine learning, identifying promising breakthroughs, new resources, data challenges, and future needs to further the utilization of machine learning in cancer applications.
In order to encourage broad participation, the workshop maintains an open call
for all interests to submit papers for consideration to present at the workshop
where computation or computational technologies has been employed effectively
in cancer research or clinical application. Lists of potential topics are
provided below, including both potential HPC technologies used in cancer
applications, and cancer applications that may use HPC technologies. With a
rapidly evolving field, authors are also encouraged to identify areas not
For a full list of topics of interest please see the complete call for papers document here.
Authors are invited to submit papers in English structured as extended abstracts from one to eight pages (not including bibliography). A bibliography should be included and use the IEEE format for conference proceedings. Submissions not conforming to these guidelines may be returned without consideration or review.
Extended abstracts will be reviewed and judged on correctness, originality, technical strength, alignment to expressed aims in the paper call, quality of presentation and interest to workshop attendees. Submitted abstracts may incorporate unpublished new advances, insight and/or original research findings.
Submissions received after the due date, exceeding the prescribed length, or not appropriately structured may also be returned without consideration or review.
In submitting the paper, the authors acknowledge that at least one author of an accepted submission will register for and attend the workshop.
Papers should be submitted electronically as PDF documents at https://easychair.org/conferences/?conf=cafcw17.