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AI Research

  • Publications

    AI-centric approach is
    what makes us different

  • Chest

    Learning Visual Context by Comparison

    Jul 15, 2020 — 2 MIN READ

    Minchul Kim et al.

  • Chest

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

    Oct 27, 2019 — 2 MIN READ

    HyunJae Lee1 et al.

  • Chest

    PseudoEdgeNet: Nuclei Segmentation only with Point Annotations

    Oct 13, 2019 — 2 MIN READ

    Inwan Yoo et al.

  • Chest

    Learning Loss for Active Learning

    Jun 16, 2019 — 2 MIN READ

    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
Breast
Chest
Oncology
  • Chest

    Learning Visual Context by Comparison

    Jul 15, 2020 — 2 MIN READ

    Minchul Kim et al.

  • Breast

    Photometric Transformer Networks and Label Adjustment for Breast Density Prediction

    Oct 27, 2019 — 2 MIN READ

    Jaehwan Lee1 et al.

  • Chest

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

    Oct 27, 2019 — 2 MIN READ

    HyunJae Lee1 et al.

  • Chest

    PseudoEdgeNet: Nuclei Segmentation only with Point Annotations

    Oct 13, 2019 — 2 MIN READ

    Inwan Yoo et al.

  • Chest

    Learning Loss for Active Learning

    Jun 16, 2019 — 2 MIN READ

    Donggeun Yoo et al.

  • Chest

    Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks

    Dec 3, 2018 — 2 MIN READ

    Hyeonseob Nam et al.

  • Chest

    CBAM: Convolutional Block Attention Module

    Sep 8, 2018 — 2 MIN READ

    Jongchan Park1 et al.

  • Chest

    BAM: Bottleneck Attention Module

    Sep 3, 2018 — 2 MIN READ

    Jongchan Park et al.

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