AI Research

  • Publications

    AI-centric approach is
    what makes us different

  • AI

    Polygonal Point Set Tracking

    CVPR (2021) — May 30, 2021

    Gunhee Nam et al.

  • AI

    Weakly-Supervised Physically Unconstrained Gaze Estimation

    CVPR (2021) — May 20, 2021

    Rakshit Kothari et al.

  • AI

    Two-Phase Learning for Weakly Supervised Object Localization

    ICCV (2017) — Dec 29, 2017

    Dahun Kim et al.

  • AI

    Multi-scale Pyramid Pooling for Deep Convolutional Representation

    CVPR Workshop (2015) — Jun 7, 2015

    Donggeun Yoo et al.

Top Rank
in A.I Competitions

VisDA

2019

*Semi - Supervised Domain Adaptation Task

  1. 1. Lunit
  2. 2. JD AI Research
  3. 3. Samsung AI Center Moscow

Camelyon

2017

  1. 1. Lunit
  2. 2. MGH CCDS
  3. 3. Eindhoven Univ. of Tech

MICCAI Grand Challenge

2016

*Tumor Proliferation Assessment

  1. 1. Lunit
  2. 2. IBM Research Zurich
  3. 5. Microsoft Research Asia

Main Task (CLS-LOC)

2015

  1. 1. Microsoft Research Asia
  2. 5. Lunit
  3. 7. Google

AI-centric Approach

We target 99% accuracy, always

We believe that the medical field is a special area that requires an utmost level of accuracy. Even a difference by 1% in accuracy can make life-saving changes.

Lunit’s goal is to always achieve 99%. And we use our unique, state-of-the-art AI training technology to achieve unprecedented accuracy.


Technology Partners


Lunit AI Publications

All
AI
  • AI

    Reducing Domain Gap by Reducing Style Bias

    CVPR (2021) — Jun 21, 2021

    Hyeonseob Nam et al.

  • AI

    Polygonal Point Set Tracking

    CVPR (2021) — May 30, 2021

    Gunhee Nam et al.

  • AI

    Weakly-Supervised Physically Unconstrained Gaze Estimation

    CVPR (2021) — May 20, 2021

    Rakshit Kothari et al.

  • AI

    Learning Visual Context by Comparison

    ECCV (2020) — Jul 15, 2020

    Minchul Kim et al.

  • AI

    Photometric Transformer Networks and Label Adjustment for Breast Density Prediction

    ICCV 2019 Workshop — Oct 27, 2019

    Jaehwan Lee1 et al.

  • AI

    SRM: A Style-based Recalibration Module for Convolutional Neural Networks

    ICCV (2019) — Oct 27, 2019

    HyunJae Lee1 et al.

  • AI

    PseudoEdgeNet: Nuclei Segmentation only with Point Annotations

    MICCAI (2019) — Oct 13, 2019

    Inwan Yoo et al.

  • AI

    Learning Loss for Active Learning

    CVPR (2019) — Jun 16, 2019

    Donggeun Yoo et al.