# Improving Medical Image Analysis with CNNs: Lunit Patent Analysis, Part 1

AI-based medical-image analysis and disease diagnosis are developing rapidly. Startups including VUNO, Lunit and JLK Inspection are researching these technologies and ways to improve analytical accuracy. This article examines AI-based medical imaging.

Source: https://www.iplexlaw.co.kr/en/blog/850029

HOME / NEWS & INSIGHTS NEWS & INSIGHTS Improving Medical Image Analysis with CNNs: Lunit Patent Analysis, Part 1 AI-based medical-image analysis and disease diagnosis are developing rapidly. Startups including VUNO, Lunit and JLK Inspection are researching these technologies and ways to improve analytical accuracy. This article examines AI-based medical imaging. AI & Software 2023.05.25 published IPLEX 3 min read Companies including VUNO, Lunit and JLK Inspection develop AI tools for medical-image analysis. This article examines a Lunit patent aimed at improving detection accuracy. Click the image to view the granted patent. The patent. The invention uses a convolutional neural network to distinguish mitotic from normal cells in images. It employs two training stages to improve the predictions. Stage 1 training The model is first trained on images labeled by a pathologist as mitotic or normal cells. Preparing for stage 2 After initial training, the model is evaluated to collect false-positive examples: normal cells incorrectly classified as mitotic. These examples are augmented, for example by rotation or added noise, and combined with the original training data. The second stage targets these false-positive errors; a mitotic cell classified as normal would instead be a false negative. Stage 2 training The CNN's weights are reinitialized and the model is trained again using the original dataset plus augmented false-positive examples. The patent describes this approach as reducing false positives compared with merely fine-tuning the existing weights. Opinions on Patents The author notes that targeted retraining may reduce false positives, while cautioning that overrepresenting those examples could bias the training set. The balance between error types should therefore be evaluated. Read the Korean source This article reflects the information available when it was published. Contact us to discuss your circumstances. Discuss this topic ↗ All articles TALK TO IPLEX Discuss your IP questions We consider your technology and business needs together. ↗ Contact us Newer Improving Medical Image Analysis with CNNs: Lunit Patent Analysis, Part 2 ↗ Older AI for Accident Prevention: Anomaly Detection and Autoencoder Patents ↗ Related insights AI & Software 2026.10.01 FiX: fine-grained forgetting in softmax attention Yongduck Kim examines FiX’s feature-wise gates, numerical implementation and paged cache, distinguishing reported gains from unresolved limitations. ↗ Read article AI & Software 2026.09.30 MHAR: Reading earlier layers through different feature subspaces Yongduck Kim examines Multi-Head Attention Residuals: depth routing, reported training results, implementation costs and the relationship between technical features and effects. ↗ Read article AI & Software 2026.09.28 Column: Claude Computer Use and the Data That Trains AI Agents Writing for AI Times, IPLEX Managing Partner Yongduck Kim examines the training data behind computer-operating AI agents through U.S. Patent No. 12,585,862. ↗ Read article

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