Skin lesion image segmentation method based on diffusion difference
The invention relates to a skin lesion image segmentation method based on diffusion difference. The method comprises the following steps: S1, acquiring a medical image data set containing multiple skin lesion types; s2, a training set X is obtained after image preprocessing; s3, building a diffusion...
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| creator | MAO SHUNQIANG YU MIANYANG CHEN YINAN ZHANG XIAOHONG ZHOU YIHAN SHUAI ZHIHAO |
| description | The invention relates to a skin lesion image segmentation method based on diffusion difference. The method comprises the following steps: S1, acquiring a medical image data set containing multiple skin lesion types; s2, a training set X is obtained after image preprocessing; s3, building a diffusion model, and training the diffusion model by using images with benign and malignant semantic information in X to obtain a trained model, S4, inputting an unlabeled skin lesion image x '0 into the trained model, and obtaining a plurality of different segmentation result sets Y of x' 0 by means of the multi-output capability of the trained model, and the uncertainty of the segmentation result is measured by means of the thought of generalized energy distance. And S5, in combination with a conditional random field algorithm, further optimizing the Y to obtain a more accurate segmentation result. According to the method, hidden semantic information is mined by learning the difference between different noises, semantic s |
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The method comprises the following steps: S1, acquiring a medical image data set containing multiple skin lesion types; s2, a training set X is obtained after image preprocessing; s3, building a diffusion model, and training the diffusion model by using images with benign and malignant semantic information in X to obtain a trained model, S4, inputting an unlabeled skin lesion image x '0 into the trained model, and obtaining a plurality of different segmentation result sets Y of x' 0 by means of the multi-output capability of the trained model, and the uncertainty of the segmentation result is measured by means of the thought of generalized energy distance. And S5, in combination with a conditional random field algorithm, further optimizing the Y to obtain a more accurate segmentation result. 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The method comprises the following steps: S1, acquiring a medical image data set containing multiple skin lesion types; s2, a training set X is obtained after image preprocessing; s3, building a diffusion model, and training the diffusion model by using images with benign and malignant semantic information in X to obtain a trained model, S4, inputting an unlabeled skin lesion image x '0 into the trained model, and obtaining a plurality of different segmentation result sets Y of x' 0 by means of the multi-output capability of the trained model, and the uncertainty of the segmentation result is measured by means of the thought of generalized energy distance. And S5, in combination with a conditional random field algorithm, further optimizing the Y to obtain a more accurate segmentation result. According to the method, hidden semantic information is mined by learning the difference between different noises, semantic s</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZHAOzs7MU8hJLc7Mz1PIzE1MT1UoTk3PTc0rSSwBCeWmlmTkpygkJRanpigA-SmZaWmlYMUgVmpRal5yKg8Da1piTnEqL5TmZlB0cw1x9tBNLciPTy0uSExOzUstiXf2MzS0MDGyNDMxdTQmRg0Ahd0ymQ</recordid><startdate>20240802</startdate><enddate>20240802</enddate><creator>MAO SHUNQIANG</creator><creator>YU MIANYANG</creator><creator>CHEN YINAN</creator><creator>ZHANG XIAOHONG</creator><creator>ZHOU YIHAN</creator><creator>SHUAI ZHIHAO</creator><scope>EVB</scope></search><sort><creationdate>20240802</creationdate><title>Skin lesion image segmentation method based on diffusion difference</title><author>MAO SHUNQIANG ; YU MIANYANG ; CHEN YINAN ; ZHANG XIAOHONG ; ZHOU YIHAN ; SHUAI ZHIHAO</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN118429645A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>MAO SHUNQIANG</creatorcontrib><creatorcontrib>YU MIANYANG</creatorcontrib><creatorcontrib>CHEN YINAN</creatorcontrib><creatorcontrib>ZHANG XIAOHONG</creatorcontrib><creatorcontrib>ZHOU YIHAN</creatorcontrib><creatorcontrib>SHUAI ZHIHAO</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>MAO SHUNQIANG</au><au>YU MIANYANG</au><au>CHEN YINAN</au><au>ZHANG XIAOHONG</au><au>ZHOU YIHAN</au><au>SHUAI ZHIHAO</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Skin lesion image segmentation method based on diffusion difference</title><date>2024-08-02</date><risdate>2024</risdate><abstract>The invention relates to a skin lesion image segmentation method based on diffusion difference. The method comprises the following steps: S1, acquiring a medical image data set containing multiple skin lesion types; s2, a training set X is obtained after image preprocessing; s3, building a diffusion model, and training the diffusion model by using images with benign and malignant semantic information in X to obtain a trained model, S4, inputting an unlabeled skin lesion image x '0 into the trained model, and obtaining a plurality of different segmentation result sets Y of x' 0 by means of the multi-output capability of the trained model, and the uncertainty of the segmentation result is measured by means of the thought of generalized energy distance. And S5, in combination with a conditional random field algorithm, further optimizing the Y to obtain a more accurate segmentation result. According to the method, hidden semantic information is mined by learning the difference between different noises, semantic s</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING IMAGE DATA PROCESSING OR GENERATION, IN GENERAL PHYSICS |
| title | Skin lesion image segmentation method based on diffusion difference |
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