Google llc (20240119586). Synthetic Generation of Clinical Skin Images in Pathology simplified abstract
Contents
- 1 Synthetic Generation of Clinical Skin Images in Pathology
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Synthetic Generation of Clinical Skin Images in Pathology - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
Synthetic Generation of Clinical Skin Images in Pathology
Organization Name
Inventor(s)
Vivek Natarajan of Mountain View CA (US)
Yuan Liu of Mountain View CA (US)
David Coz of Mountain View CA (US)
Amirata Ghorbani of Mountain View CA (US)
Synthetic Generation of Clinical Skin Images in Pathology - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240119586 titled 'Synthetic Generation of Clinical Skin Images in Pathology
Simplified Explanation
The patent application describes the use of generative adversarial networks (GAN) to create synthetic clinical images of skin conditions. These synthetic images can be customized in terms of size, location, and skin color, and are evaluated to be of high fidelity compared to real images.
- Skin condition synthesis using GAN:
* Generation and training of GAN to create synthetic clinical images of skin conditions * Ability to customize size, location, and skin color of the synthetic images * High fidelity of the generated images demonstrated through objective evaluation metrics
- Data augmentation for skin condition classifier:
* Use of synthetic skin images as a data augmentation technique for training a skin condition classifier * Improvement in the classifier's ability to detect rare but malignant conditions
Potential Applications
The technology can be applied in medical education, telemedicine, and dermatology research.
Problems Solved
The technology addresses the need for diverse and realistic clinical images for training and testing skin condition classifiers.
Benefits
The innovation provides a cost-effective and efficient way to generate synthetic clinical images for various skin conditions, improving the accuracy of diagnostic tools.
Potential Commercial Applications
"Synthetic Skin Image Generation for Dermatology Applications" can be used in healthcare software development, medical imaging companies, and dermatology clinics.
Possible Prior Art
Prior art may include research on image synthesis using GANs in various medical fields, such as radiology and ophthalmology.
Unanswered Questions
How does the technology handle variations in skin tones and textures?
The article does not provide specific details on how the GANs account for different skin tones and textures when generating synthetic images.
What are the limitations of using synthetic images for training skin condition classifiers?
The article does not discuss any potential drawbacks or challenges associated with using synthetic images as a data augmentation technique in training classifiers.
Original Abstract Submitted
we disclose the generation and training of generative adversarial networks (gan) to synthesize clinical images with skin conditions. synthetic images for a pre-specified skin condition are generated, while being able to vary its size, location and the underlying skin color. we demonstrate that the generated images are of high fidelity using objective gan evaluation metrics. the synthetic images are not only visually similar to real images, but also embody the respective skin conditions. additionally, synthetic skin images can be used as a data augmentation technique for training a skin condition classifier, and improve the ability of the classifier to detect rare but malignant conditions.
