Publications:

Yang F, Zamzmi G, Angara S, Rajaraman S, Aquilina A, Xue Z, Jaeger S, Papagiannakis E, Antani SK. Assessing Inter-Annotator Agreement for Medical Image Segmentation. IEEE Access, doi: 10.1109/ACCESS.2023.3249759.

Xue Z, Angara S, Guo P, Rajaraman S, Jeronimo J, Rodriguez AC, Alfaro K, Charoenkwan K, Mungo C, Fokom‐Domgue J, Wentzensen N, Desai K, Ajenifuja K, Wikström E, Befano BS, Antani S. Image Quality Classification for Automated Visual Evaluation of Cervical Precancer. Zamzmi, G., Antani, S., Bagci, U., Linguraru, M.G., Rajaraman, S., Xue, Z. (eds) Medical Image Learning with Limited and Noisy Data. MILLanD 2022. Lecture Notes in Computer Science, vol 13559. Springer, Cham. https://doi.org/10.1007/978-3-031-16760-7_20.

Angara S, Guo P, Xue Z, Antani S. An Empirical Study of Vision Transformers for Cervical Precancer Detection. In: Santosh, K., Hegadi, R., Pal, U. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2021. Communications in Computer and Information Science, vol 1576. Springer, Cham. https://doi.org/10.1007/978-3-031-07005-1_3.

Guo P, Xue Z, Angara S, Antani SK . Unsupervised Deep Learning Registration of Uterine Cervix Sequence Images. Cancers, 14(10), 2401. https://doi.org/10.3390/cancers14102401.

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