Recent updates

Program update
Submissions opens on: 2nd May 2024

Workshop Description

Data engineering plays a vital role in advancing medical imaging research, where limited data availability poses a significant challenge. To tackle this issue, the medical imaging community has adopted various techniques, including active learning, label and data augmentation, self-supervision, and synthetic data generation.

However, the potential of these methods has yet to be fully leveraged. Data augmentation, for instance, is often randomly chosen based on intuition. Yet previous work has shown that it is crucial to jointly optimize the augmentations' complexity and affinity, i.e., how much the augmentation shifts the decision boundary of the clean baseline model. Other studies suggest that not every synthetic example improves the model's generalizability, with some even hurting performance if not reasonably chosen. Similarly, the effect of self-supervised pre-training methods on downstream tasks generally depends on the overlap between the pretext task and the downstream tasks. For instance, a model trained to predict rotation angles will not be effective in rotationally invariant organs.

We witness various research efforts in the data engineering domain, but most are pursued in isolation. And many research questions in data engineering that are of utmost importance to the success of machine learning methods for medical image analysis remain unanswered. Some questions this workshop hopes to help answer are:

What data should we collect to train a model for a given medical task?
How do we identify diverse and discriminative examples for a downstream task in complex biomedical datasets? For instance, do we need to annotate every frame in a surgical video?
How do we identify the best augmentation strategy?
What is the best way to spend a data labeling budget (time/money)? How much data should we collect, and how much of it should we label?
How do we learn to synthesize data for a downstream medical task?
How do we design suitable pretext tasks for a particular downstream task?
And overall, how can we blend exisitng data-focused works to extract the most benefit from the data?

The workshop invites researchers to submit their work in the field of medical imaging around the central theme of data engineering. Some themes we would like to explore include for instance:

Workshop themes

Data Augmentation and Label Augmentation in the Medical Domain: includes data augmentation through geometric transformations or application-aware policies. It also investigates data generated from virtual environments, phantoms, or generative models.
Active Learning and Active Synthesis: covers methods that find the most discriminative and diverse subset of unlabelled data to train a model for various clinical applications. Active synthesis is about generating synthetic data for a particular application.
Federated learning: distributed data management and learning to address privacy concerns and security, for instance, across institutions or countries.
Multimodal learning: includes approaches to combine data from multiple sources and sensors (e.g. CT, MRI, endoscope, text, audio, depth, etc.)
Self-Supervised Learning Algorithms for Medical Downstream Tasks: investigates application-specific relevant pretext tasks for pre-training models in a self-supervised manner.
Large-scale data management and data quality assessment.

Keynote speakers

Prof. Alejandro Frangi

Alejandro Frangi is a prominent researcher in the field of medical image analysis and modeling, with a focus on machine learning and computational physiology. His research interests lie at the intersection of medical image analysis, machine learning, and computational physiology, with a particular emphasis on statistical methods applied to population imaging and in silico clinical trials. His work has been highly interdisciplinary, spanning the areas of cardiovascular, musculoskeletal, and neurosciences. Professor Frangi's research has been instrumental in advancing the field of medical image analysis and computational modeling, leading to the development of three spin-off companies: Clintelis SA in 2009, GalgoMedical SA in 2013, and adsilico Ltd in 2022. His contributions have been recognized through numerous awards and honors, including being named an IEEE Fellow (2014), EAMBES Fellow (2013), SPIE Fellow (2020), AAIA-AI Fellow (2021), MICCAI Fellow (2021), ATI Turing Fellow (2021), and FREng (2023). He has also served in various leadership roles, including serving on the Board of Directors of the Medical Image Computing and Computer-Assisted Interventions Society (MICCAI) from 2014 to 2019 and as Chair of the Editorial Board of the MICCAI-Elsevier Book Series from 2017 to 2023.

Prof. Bernhard Kainz

Bernhard Kainz is a distinguished academic figure with a robust background in the field of Medical Image Computing. Holding the position of Associate Professor in the Department of Computing at Imperial College London, he leads the human-in-the-loop computing group and co-leads the biomedical image analysis research group (BioMedIA). Additionally, he serves as a Professor at Friedrich-Alexander-University Erlangen-Nuremberg, where he heads the Image Data Exploration and Analysis Lab (IDEA Lab). His research primarily focuses on intelligent algorithms in healthcare, particularly in Medical Imaging, with a special emphasis on self-driving medical image acquisition that can assist human operators in real-time diagnostics.

Important Dates

Paper submission begins: 2nd May 2024

Submission deadline: 24th June 2024

Paper decision notification: 15th July 2024

Camera ready submission: 1st August 2024

Workshop day: 10th October 2024

Submission

CMT submission website: CMT for DEMI@MICCAI2024

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 MICCAI2024 anonymity guidelines when preparing your intial submission.

Organising committee


Binod Bhattarai University of Aberdeen, UK	Sharib Ali University of Leeds, UK	Anita Rau Stanford University, USA

Razvan Caramalau University College London, UK	Anh Nguyen University of Liverpool, UK	Prashnna Gyawali West Virginia University, USA

Ana Namburete University of Oxford, UK	Danail Stoyanov University College London, UK