# Improving Medical Image Analysis with CNNs: Lunit Patent Analysis, Part 2

Following an earlier analysis of Lunit's medical-imaging patents, this article examines another CNN-based approach: analyzing cell images to identify the number and location of mitotic cells.

Source: https://www.iplexlaw.co.kr/en/blog/850833

HOME / NEWS & INSIGHTS NEWS & INSIGHTS Improving Medical Image Analysis with CNNs: Lunit Patent Analysis, Part 2 Following an earlier analysis of Lunit's medical-imaging patents, this article examines another CNN-based approach: analyzing cell images to identify the number and location of mitotic cells. AI & Software 2023.05.26 published IPLEX 3 min read Following an earlier analysis of Lunit's medical-imaging patents, this article examines another CNN-based approach: analyzing cell images to identify the number and location of mitotic cells. The previous article examined training a CNN to improve detection accuracy. This article examines how a CNN identifies the number and locations of mitotic cells. The patent. Click the image to view the granted patent. A cell image is supplied to the first convolutional layer to extract a feature map. The second convolutional layer processes that feature map and produces the first class activation map. This map first class activation map. represents features associated with mitotic cells. first class activation map. It is then first class activation map. processed through the output stage using global pooling, such as max or average pooling. first class activation map. The resulting second class activation map is used in the following step. second class activation map The map is then enlarged second class activation map because convolutional processing has reduced its spatial dimensions relative to the input image. second class activation map To locate the mitotic cells in the original image, second class activation map the activation map is resized to match the input's dimensions. This is called the third class activation map in the description. third class activation map The map is then enlarged third class activation map Regions exceeding a threshold or containing a local peak are identified; the illustration shows them in darker colors. third class activation map Those regions indicate candidate mitotic-cell locations in the original image. Counting the regions estimates the number of mitotic cells. Although the underlying CNN operations are familiar, the patent describes their use for locating and counting mitotic cells. That specific application is central to the analysis. 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 SUALAB Patent Analysis: Anomaly Detection ↗ Older Improving Medical Image Analysis with CNNs: Lunit Patent Analysis, Part 1 ↗ 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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