Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.01391 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929524169506816 |
|---|---|
| author | Kindo, Toshiki |
| author_facet | Kindo, Toshiki |
| contents | In this paper, we present a new statistical approach to automatically identify cancer regions in pathological images. The proposed method is built from statistical theory in line with evidence-based medicine. The two core technologies are the classification information of image features, which was introduced based on information theory and which cancer features take positive values, normal features take negative values, and the calculation technique for determining their spatial distribution. This method then estimates areas where the classification information content shows a positive value as cancer areas in the pathological image. The method achieves AUCs of 0.95 or higher in cancer classification tasks. In addition, the proposed method has the practical advantage of not requiring a precise demarcation line between cancer and normal. This frees pathologists from the monotonous and tedious work of building consensus with other pathologists. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01391 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantifying Cancer Likeness: A Statistical Approach for Pathological Image Diagnosis Kindo, Toshiki Computer Vision and Pattern Recognition In this paper, we present a new statistical approach to automatically identify cancer regions in pathological images. The proposed method is built from statistical theory in line with evidence-based medicine. The two core technologies are the classification information of image features, which was introduced based on information theory and which cancer features take positive values, normal features take negative values, and the calculation technique for determining their spatial distribution. This method then estimates areas where the classification information content shows a positive value as cancer areas in the pathological image. The method achieves AUCs of 0.95 or higher in cancer classification tasks. In addition, the proposed method has the practical advantage of not requiring a precise demarcation line between cancer and normal. This frees pathologists from the monotonous and tedious work of building consensus with other pathologists. |
| title | Quantifying Cancer Likeness: A Statistical Approach for Pathological Image Diagnosis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.01391 |