Special Session on ICONIP 2022

Learning with Fewer Labels in Medical Computing


Scope, aims and topics

In medicine and healthcare, we often don’t have enough labelled data due to the great efforts required in annotation, thus requiring models that are able to (a) learn with few annotated examples, and (b) continually adapt to new data without forgetting the prior knowledge. Luckily, we can learn from just few examples (i.e., few shot learning), do long-term learning, and form abstract models of a situation and manipulate these models to achieve extreme generalization. As a result, one of the next big challenges in medical computing is to develop effective learning-based methods that are capable of addressing the important shortcomings of existing techniques in this regard. Therefore, there is pressing need for novel research methods, (1) to drastically reduce require-ments for labeled training data, (2) to significantly reduce the amount of data necessary to adapt the model to new environment, and (3) to even use as little labeled training data as we need.

This special session focuses on the development of deep learning methods with fewer labels for medical computing. The topics of interest include (but are not limited to) the following areas:

  • Few label learning in medical visual question and answering

  • Generating diagnostic reports from medical images

  • Incremental learning in NLP

  • Generating reports from clinical notes

  • Embedding medical knowledge in diagnosis

  • Knowledge transfer under various clinical environments

  • Image classification with MRI/CT/PET/mesh/point cloud etc

  • Learning robust medical image representation with noisy annotation

  • Un-/semi-/weakly-supervised medical data analysis

  • Predicting clinical outcomes from multimodal medical data

  • Anomaly detection in medical data

  • Meta-learning in medical computing

  • Active learning and life-long learning in medical computing

  • Zero/few-shot learning in medical computing


Important dates:

Submission of papers: 11:59pm (AoE), June 15, 2022

Acceptance notification: 11:59pm (AoE), August 15, 2022

Camera ready: 11:59pm (AoE), August 31, 2022

Conference date: 11:59pm (AoE), November 22-26, 2022


Organizers

Imran Razzak, University of New South Wales, Australia

Xuequan Lu, Deakin University, Australia

Xiao Liu, Deakin University, Australia


Please submit using the guidelines in ICONIP 2022.