Multi-test cervical cancer diagnosis with missing data estimation

Tao Xu; Xiaolei Huang; Edward Kim; L. Rodney Long; Sameer Antani

doi:10.1117/12.2080871

20 March 2015 Multi-test cervical cancer diagnosis with missing data estimation

Tao Xu, Xiaolei Huang, Edward Kim, L. Rodney Long, Sameer Antani

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 94140X (2015) https://doi.org/10.1117/12.2080871
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

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.

Citation Download Citation

Tao Xu, Xiaolei Huang, Edward Kim, L. Rodney Long, and Sameer Antani "Multi-test cervical cancer diagnosis with missing data estimation", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94140X (20 March 2015); https://doi.org/10.1117/12.2080871

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