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Litingyu Wang

Wlty

  •  China
  •  University of Electronic Science and Technology of China
  •  School of Mechanical and Electrical Engineering
Statistics
  • Member for 1 year, 5 months
  • 80 challenge submissions
  • 4 algorithms run

Activity Overview

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LNQ2023
Challenge User

Accurate lymph node size estimation is critical for staging cancer patients, initial therapeutic management, and in longitudinal scans, assessing response to therapy. Current standard practice for quantifying lymph node size is based on a variety of criteria that use unidirectional or bidirectional measurements on just one or a few nodes, typically on just one axial slice. But humans have hundreds of lymph nodes, any number of which may be enlarged to various degrees due to disease or immune response. While a normal lymph node may be approximately 5mm in diameter, a diseased lymph node may be several cm in diameter. The mediastinum, the anatomical area between the lungs and around the heart, may contain ten or more lymph nodes, often with three or more enlarged greater than 1cm. Accurate segmentation in 3D would provide more information to evaluate lymph node disease.

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Foundation Model Prompting for Medical Image Classification
Challenge User

The primary objective of this challenge is to promote the development and evaluation of model adaptation techniques for medical image classification to leverage the existing foundation models.

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Head and Neck Tumor Segmentation for MR-Guided Applications
Challenge User

This challenge focuses on developing algorithms to automatically segment head and neck cancer gross tumor volumes on multi-timepoint MRI