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hui lin

huilin

  •  United States of America
  •  northwestern university
  •  electrial engineering
Statistics
  • Member for 10 months, 3 weeks
  • 93 challenge submissions
  • 68 algorithms run

Activity Overview

CHAOS Logo
CHAOS
Challenge Participant

In this challenge, you segment the liver in CT data, and segment liver, spleen, and kidneys in MRI data.

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The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge
Challenge Participant

A semantic multimodal segmentation challenge comprising 30 organs at risk

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ARCADE-MICCAI2023
Challenge Participant

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Universal Lesion Segmentation Challenge '23
Challenge Participant

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Justified Referral in AI Glaucoma Screening
Challenge Participant

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Light My Cells : Bright Field to Fluorescence Imaging Challenge
Challenge Participant

Join the Light My Cells France-Bioimaging challenge! Enhance biology and microscopy by contributing to the development of new image-to-image deep labelling methods. The task: predict the best-focused output images of several fluorescently labelled organelles from label-free transmitted light input images. Dive into the future of imaging with us! 🌐🔬 #LightMyCellsChallenge

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Diminished Reality for Emerging Applications in Medicine
Challenge Participant

The Diminished Reality for Emerging Applications in Medicine through Inpainting (DREAMING) challenge seeks to pioneer the integration of Diminished Reality (DR) into oral and maxillofacial surgery. While Augmented Reality (AR) has been extensively explored in medicine, DR remains largely uncharted territory. DR involves virtually removing real objects from the environment by replacing them with their background. Recent inpainting methods present an opportunity for real-time DR applications without scene knowledge. DREAMING focuses on implementing such methods to fill obscured regions in surgery scenes with realistic backgrounds, emphasizing the complex facial anatomy and patient diversity. The challenge provides a dataset of synthetic yet photorealistic surgery scenes featuring humans, simulating an operating room setting. Participants are tasked with developing algorithms that seamlessly remove disruptions caused by medical instruments and hands, offering surgeons an unimpeded view of the operative site.

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AutoPET III
Challenge Participant

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Pelvic Bone Fragments with Injuries Segmentation Challenge
Challenge Participant

Pelvic fracture segmentation in CT and X-ray