TY - GEN
T1 - Image Quality Classification for Automated Visual Evaluation of Cervical Precancer
AU - Xue, Zhiyun
AU - Angara, Sandeep
AU - Guo, Peng
AU - Rajaraman, Sivaramakrishnan
AU - Jeronimo, Jose
AU - Rodriguez, Ana Cecilia
AU - Alfaro, Karla
AU - Charoenkwan, Kittipat
AU - Mungo, Chemtai
AU - Domgue, Joel Fokom
AU - Wentzensen, Nicolas
AU - Desai, Kanan T.
AU - Ajenifuja, Kayode Olusegun
AU - Wikström, Elisabeth
AU - Befano, Brian
AU - de Sanjosé, Silvia
AU - Schiffman, Mark
AU - Antani, Sameer
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Image quality control is a critical element in the process of data collection and cleaning. Both manual and automated analyses alike are adversely impacted by bad quality data. There are several factors that can degrade image quality and, correspondingly, there are many approaches to mitigate their negative impact. In this paper, we address image quality control toward our goal of improving the performance of automated visual evaluation (AVE) for cervical precancer screening. Specifically, we report efforts made toward classifying images into four quality categories (“unusable”, “unsatisfactory”, “limited”, and “evaluable”) and improving the quality classification performance by automatically identifying mislabeled and overly ambiguous images. The proposed new deep learning ensemble framework is an integration of several networks that consists of three main components: cervix detection, mislabel identification, and quality classification. We evaluated our method using a large dataset that comprises 87,420 images obtained from 14,183 patients through several cervical cancer studies conducted by different providers using different imaging devices in different geographic regions worldwide. The proposed ensemble approach achieved higher performance than the baseline approaches.
AB - Image quality control is a critical element in the process of data collection and cleaning. Both manual and automated analyses alike are adversely impacted by bad quality data. There are several factors that can degrade image quality and, correspondingly, there are many approaches to mitigate their negative impact. In this paper, we address image quality control toward our goal of improving the performance of automated visual evaluation (AVE) for cervical precancer screening. Specifically, we report efforts made toward classifying images into four quality categories (“unusable”, “unsatisfactory”, “limited”, and “evaluable”) and improving the quality classification performance by automatically identifying mislabeled and overly ambiguous images. The proposed new deep learning ensemble framework is an integration of several networks that consists of three main components: cervix detection, mislabel identification, and quality classification. We evaluated our method using a large dataset that comprises 87,420 images obtained from 14,183 patients through several cervical cancer studies conducted by different providers using different imaging devices in different geographic regions worldwide. The proposed ensemble approach achieved higher performance than the baseline approaches.
KW - Automated visual evaluation
KW - Ensemble learning
KW - Image quality
KW - Mislabel identification
KW - Uterine cervix image
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U2 - 10.1007/978-3-031-16760-7_20
DO - 10.1007/978-3-031-16760-7_20
M3 - Conference contribution
C2 - 36315110
AN - SCOPUS:85140477049
SN - 9783031167591
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 206
EP - 217
BT - Medical Image Learning with Limited and Noisy Data - 1st International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, Proceedings
A2 - Zamzmi, Ghada
A2 - Antani, Sameer
A2 - Rajaraman, Sivaramakrishnan
A2 - Xue, Zhiyun
A2 - Bagci, Ulas
A2 - Linguraru, Marius George
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st International Workshop on Medical Image Learning with Limited and Noisy Data, MILLanD 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Y2 - 22 September 2022 through 22 September 2022
ER -