MICCAI Workshop on
2nd Resource-Efficient Medical Image Analysis (REMIA)

Recent Updates

• [07/10/23] The zoom link of the REMIA2 workshop + ATLAS challenge is: https://ualberta-ca.zoom.us/j/6492916737?pwd=NGFCaE4wV0o3MVZzN3dpYkloYjZpQT09
  Meeting ID: 649 291 6737
  Passcode: VisionLear
• [04/10/23] The REMIA Programme has been released: >>>Programme. See you in 8th Oct.
• [01/05/23] In this year, our REMIA workshop will be held in conjunction with "A Tumor and Liver Automatic Segmentation (ATLAS)" Challenge in MICCAI. [Challenge Link].
• [01/05/23] Submission system is opened: [Submission Link]
• [15/04/23] In this year, our REMIA workshop will be held by online only.
• [25/02/23] Our 2nd REMIA workshop has been approval by MICCAI 2023. See you in Vancouver/CANADA!

About REMIA Workshop

Resource-Efficient Medical Image Analysis (REMIA) is a workshop on International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) In this year, our REMIA workshop will be held in conjunction with "A Tumor and Liver Automatic Segmentation (ATLAS)" Challenge.

Overview

Deep learning methods have shown remarkable success in many medical imaging tasks over the past few years. However, there exists a challenge that current deep learning models are usually data-hungry, requiring massive amounts of high-quality annotated data to achieve high performance.

Firstly, collecting large scale medical imaging datasets is usually expensive and time-consuming, and the regulatory and governance also raise additional challenges for practical healthcare applications. Moreover, the domain shifts in medical data caused by factors such as different medical devices, different subject cohorts and different scanning configurations and conditions have been challenging for deploying the AI models for real-world applications. Secondly, acquiring the data annotations is even more of a challenge as the experienced and knowledgeable clinicians are required to provide high quality annotations. The annotation process is labour-intensive and time-consuming when it comes to the segmentation tasks, especially for 3D medical data, such as CT, OCT and MRI scans etc.. Minutes to hours may be required for the clinicians to annotate single image, given the complexity of the segmentation tasks. Thirdly, it is infeasible to deploy large deep learning models to edge devices for various medical tasks within a low-resource situation, especially with hardware constraints for practical clinical applications in the era of Telehealth and Metaverse.

The vanilla deep learning models usually have limited ability of learning from sparse training samples. Consequently, to enable efficient and practical deep learning models for medical imaging, there is a need for research methods that can handle limited number of training data, limited labels and limited hardware constraints when deploying the model. To address the limited data challenge, recent methods related to data efficiency such as transfer learning, domain adaptation that can mitigate the domain shifts problem in medical imaging have been proposed in medical image analysis field. Besides, label efficiency methods such as partially-supervised learning, annotation-efficient learning and weakly supervised learning methods including semi-supervised, unsupervised, self-supervised as well as contrastive learning have been widely studied in this field including recent published work in MICCAI conferences etc. However, hardware efficiency related topics such as neural network compression, neural architecture search, etc has not been fully explored in the field. Therefore, in this workshop, we will encourage submissions on this topic to discuss the potential research problems raised by hardware efficiency, preparing for more AI applications for Metaverse and Telemedicine.

Details

We propose the below research topics from data, annotation and hardware perspectives for medical image analysis. Topics of interest include, but are not limited to:

• Data efficiency:
  • Transfer learning;
  • Unsupervised domain adaptation;
  • Single-shot/One-shot/Few-shot learning methods;
  • Medical image classification/segmentation with small training dataset;
• Label efficiency:
  • Partial annotation/label learning methods;
  • Weakly-supervised learning methods;
  • Semi-supervised learning methods;
  • Unsupervised learning methods;
  • Self-supervised learning methods;
  • Contrastive learning;
• Hardware efficiency:
  • Neural network compression;
  • Knowledge distillation;
  • Neural architecture search;
  • Lightweight network design for medical image analysis;
• Resource-efficient learning for real-world applications:
  • Model deployment on low-resource devices;
  • Knowledge distillation;
  • Disease diagnosis, progression and treatment stratification with limited training data;
  • New datasets and benchmark for resource-efficient learning in medical image analysis;