International business machines corporation (20240193787). SIMULATING PROGRESSION OF SKIN CONDITIONS BASED ON MACHINE LEARNING simplified abstract
Contents
- 1 SIMULATING PROGRESSION OF SKIN CONDITIONS BASED ON MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SIMULATING PROGRESSION OF SKIN CONDITIONS BASED ON MACHINE LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Skin Condition Visualization Technology
- 1.13 Original Abstract Submitted
SIMULATING PROGRESSION OF SKIN CONDITIONS BASED ON MACHINE LEARNING
Organization Name
international business machines corporation
Inventor(s)
Yuan Yuan Ding of Shanghai (CN)
Yi Chen Zhong of Shanghai (CN)
Yang Liu of Zhong Xin City (CN)
SIMULATING PROGRESSION OF SKIN CONDITIONS BASED ON MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240193787 titled 'SIMULATING PROGRESSION OF SKIN CONDITIONS BASED ON MACHINE LEARNING
Simplified Explanation
The patent application discusses techniques for visualizing skin conditions using machine learning. It involves retrieving a color image of facial skin, generating a monochromatic version, segmenting skin condition instances based on a machine learning model, filtering them using a polarized version, and generating simulation images.
- Color image of facial skin retrieved
- Monochromatic version generated
- Skin condition instances segmented based on a machine learning model
- Filtering of instances using a polarized version
- Simulation images generated based on filtered instances
Key Features and Innovation
- Utilizes machine learning for skin condition visualization - Incorporates color, monochromatic, and polarized images for analysis - Segmentation and filtering techniques enhance accuracy of identifying skin conditions
Potential Applications
- Dermatology clinics for skin condition diagnosis - Beauty industry for personalized skincare recommendations - Medical research for studying skin diseases
Problems Solved
- Improved visualization of skin conditions - Enhanced accuracy in identifying skin conditions - Efficient analysis of facial skin images
Benefits
- Early detection of skin conditions - Personalized treatment recommendations - Streamlined skin analysis process
Commercial Applications
Title: Advanced Skin Condition Visualization Technology for Dermatology Clinics This technology can be used in dermatology clinics to enhance skin condition diagnosis and treatment recommendations, leading to improved patient outcomes and customer satisfaction.
Prior Art
Research on machine learning-based skin condition analysis and visualization techniques in the field of dermatology.
Frequently Updated Research
Ongoing studies on the application of machine learning in dermatology for skin condition analysis and diagnosis.
Questions about Skin Condition Visualization Technology
How does machine learning improve skin condition visualization?
Machine learning algorithms can analyze large amounts of data to accurately identify and segment skin conditions in images, providing more precise results than traditional methods.
What are the potential limitations of using polarized images in skin condition visualization?
Polarized images may not always capture all aspects of skin conditions accurately, leading to potential inaccuracies in the filtering process.
Original Abstract Submitted
techniques for skin-condition visualization using machine learning. a color image depicting facial skin of a subject is retrieved. a monochromatic version of the color image is generated. candidate instances of one or more skin conditions are segmented from the monochromatic version based on a segmentation threshold and using a machine learning model. a polarized version of the color image is generated, and based on the polarized version, the candidate instances are filtered. one or more simulation images are generated based on the filtered candidate instances.
