Abnormality Detection in Retinal Images
The implementation of data mining techniques in the medical area has generated great interest because of its potential for more efficient, economic and robust performance when compared to physicians. In this paper, we focus on the implementation of Multiple-Instance Learning (MIL) in the area of med...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | en_US |
| Published: |
2003
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/3845 |
| _version_ | 1811095213712605184 |
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| author | Yu, Xiaoxue Hsu, Wynne Lee, Wee Sun Lozano-Pérez, Tomás |
| author_facet | Yu, Xiaoxue Hsu, Wynne Lee, Wee Sun Lozano-Pérez, Tomás |
| author_sort | Yu, Xiaoxue |
| collection | MIT |
| description | The implementation of data mining techniques in the medical area has generated great interest because of its potential for more efficient, economic and robust performance when compared to physicians. In this paper, we focus on the implementation of Multiple-Instance Learning (MIL) in the area of medical image mining, particularly to hard exudates detection in retinal images from diabetic patients. Our proposed approach deals with the highly noisy images that are common in the medical area, improving the detection specificity while keeping the sensitivity as high as possible. We have also investigated the effect of feature selection on system performance. We describe how we implement the idea of MIL on the problem of retinal image mining, discuss the issues that are characteristic of retinal images as well as issues common to other medical image mining problems, and report the results of initial experiments. |
| first_indexed | 2024-09-23T16:12:26Z |
| format | Article |
| id | mit-1721.1/3845 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:12:26Z |
| publishDate | 2003 |
| record_format | dspace |
| spelling | mit-1721.1/38452019-04-12T08:06:52Z Abnormality Detection in Retinal Images Yu, Xiaoxue Hsu, Wynne Lee, Wee Sun Lozano-Pérez, Tomás data mining abnormality detection multiple-instance learning medical image mining The implementation of data mining techniques in the medical area has generated great interest because of its potential for more efficient, economic and robust performance when compared to physicians. In this paper, we focus on the implementation of Multiple-Instance Learning (MIL) in the area of medical image mining, particularly to hard exudates detection in retinal images from diabetic patients. Our proposed approach deals with the highly noisy images that are common in the medical area, improving the detection specificity while keeping the sensitivity as high as possible. We have also investigated the effect of feature selection on system performance. We describe how we implement the idea of MIL on the problem of retinal image mining, discuss the issues that are characteristic of retinal images as well as issues common to other medical image mining problems, and report the results of initial experiments. Singapore-MIT Alliance (SMA) 2003-12-13T18:09:51Z 2003-12-13T18:09:51Z 2004-01 Article http://hdl.handle.net/1721.1/3845 en_US Computer Science (CS); 274000 bytes application/pdf application/pdf |
| spellingShingle | data mining abnormality detection multiple-instance learning medical image mining Yu, Xiaoxue Hsu, Wynne Lee, Wee Sun Lozano-Pérez, Tomás Abnormality Detection in Retinal Images |
| title | Abnormality Detection in Retinal Images |
| title_full | Abnormality Detection in Retinal Images |
| title_fullStr | Abnormality Detection in Retinal Images |
| title_full_unstemmed | Abnormality Detection in Retinal Images |
| title_short | Abnormality Detection in Retinal Images |
| title_sort | abnormality detection in retinal images |
| topic | data mining abnormality detection multiple-instance learning medical image mining |
| url | http://hdl.handle.net/1721.1/3845 |
| work_keys_str_mv | AT yuxiaoxue abnormalitydetectioninretinalimages AT hsuwynne abnormalitydetectioninretinalimages AT leeweesun abnormalitydetectioninretinalimages AT lozanopereztomas abnormalitydetectioninretinalimages |