Publications
AI-centric approach is
what makes us differentLearning Visual Context by Comparison
SRM: A Style-based Recalibration Module for Convolutional Neural Networks
PseudoEdgeNet: Nuclei Segmentation only with Point Annotations
Learning Loss for Active Learning
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.
Learning Visual Context by Comparison
Photometric Transformer Networks and Label Adjustment for Breast Density Prediction
SRM: A Style-based Recalibration Module for Convolutional Neural Networks
PseudoEdgeNet: Nuclei Segmentation only with Point Annotations
Learning Loss for Active Learning
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
CBAM: Convolutional Block Attention Module
BAM: Bottleneck Attention Module