# AI Preprocessing Technology: JLK Inspection Patent Analysis

Removing noise from training data matters: models trained on noisy data can be less accurate.

Source: https://www.iplexlaw.co.kr/en/blog/849033

HOME / NEWS & INSIGHTS NEWS & INSIGHTS AI Preprocessing Technology: JLK Inspection Patent Analysis Removing noise from training data matters: models trained on noisy data can be less accurate. AI & Software 2023.05.23 published IPLEX 4 min read Removing noise from training data matters: models trained on noisy data can be less accurate. This article examines morphological erosion and dilation and their use in reducing annotation noise. Erosion / Dilation These operations use a structuring element, or kernel, to process a binary image. Its shape and size should suit the features to be preserved. Erosion With black treated as foreground value 1 and white as background value 0 in this illustration, erosion retains a foreground pixel only when the relevant kernel neighborhood satisfies the foreground condition. It shrinks foreground regions. Dilation Dilation expands the foreground: an output location becomes foreground when the kernel overlaps foreground in the input. It can restore regions after erosion while leaving removed small noise components absent. How to use Erosion and Dilation for noise reduction in training data The following JLK Inspection patent applies these operations to noise in MRI training annotations. If you click on the image, you can see the patent notice. For supervised lesion detection, clinicians annotate the lesion regions in images used for training. Finger or mouse annotation can accidentally include areas outside a lesion, especially when the target is small. These erroneous regions introduce noise into the training labels. The patent description converts the annotated MRI data into a three-dimensional sinogram. Connected regions represent the intended lesion, while isolated points, shown in red, represent noise. Three-dimensional sinogram containing noise Erosion and dilation remove small unwanted components from that representation, producing the cleaned result shown below. Three-dimensional sinogram after noise removal The cleaned representation is converted back into MRI training data. The proposed preprocessing aims to improve the quality of data used to train the analysis model. This illustrates how a specific adaptation of a known technique to a technical problem can be assessed for patent protection. 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 AI for Accident Prevention: Anomaly Detection and Autoencoder Patents ↗ Older IPLEX Signs an MOU with 2hrs Academy ↗ 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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