Conference proceeding
Multi-Test Cervical Cancer Diagnosis with Missing Data Estimation
MEDICAL IMAGING 2015: COMPUTER-AIDED DIAGNOSIS, v 9414, pp 94140X-94140X-8
01 Jan 2015
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Cervical cancer is a leading most common type of cancer for women worldwide. Existing screening programs for cervical cancer suffer from low sensitivity. Using images of the cervix (cervigrams) as an aid in detecting pre-cancerous changes to the cervix has good potential to improve sensitivity and help reduce the number of cervical cancer cases. In this paper, we present a method that utilizes multi-modality information extracted from multiple tests of a patient's visit to classify the patient visit to be either low-risk or high-risk. Our algorithm integrates image features and text features to make a diagnosis. We also present two strategies to estimate the missing values in text features: Image Classifier Supervised Mean Imputation (ICSMI) and Image Classifier Supervised Linear Interpolation (ICSLI). We evaluate our method on a large medical dataset and compare it with several alternative approaches. The results show that the proposed method with ICSLI strategy achieves the best result of 83.03% specificity and 76.36% sensitivity. When higher specificity is desired, our method can achieve 90% specificity with 62.12% sensitivity.
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Details
- Title
- Multi-Test Cervical Cancer Diagnosis with Missing Data Estimation
- Creators
- Tao Xu - Lehigh UniversityXiaolei Huang - Lehigh UniversityEdward Kim - Villanova UniversityL. Rodney Long - United States National Library of MedicineSameer Antani - United States National Library of Medicine
- Contributors
- L M Hadjiiski (Editor)G D Tourassi (Editor)
- Publication Details
- MEDICAL IMAGING 2015: COMPUTER-AIDED DIAGNOSIS, v 9414, pp 94140X-94140X-8
- Series
- Proceedings of SPIE
- Publisher
- Spie-Int Soc Optical Engineering
- Number of pages
- 8
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:000357728600031
- Scopus ID
- 2-s2.0-84948778691
- Other Identifier
- 991021884691404721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Optics
- Radiology, Nuclear Medicine & Medical Imaging