Research

Interests

Kanavati, F., Toyokawa, G., Momosaki, S., Rambeau, M., Kozuma, Y., Shoji, F., Yamazaki, K., Takeo, S., Iizuka, O. and Tsuneki, M., 2020. Weakly-supervised learning for lung carcinoma classification using deep learning. Scientific Reports, 10(1), pp.1-11.
Iizuka, O.*, Kanavati, F.*, Kato, K.*, Rambeau, M., Arihiro, K., & Tsuneki, M. (2020). Deep Learning Models for Histopathological Classification of Gastric and colonic epithelial tumours. Scientific Reports, 10(1), 1-11.
Kanavati, F., Misawa, K., Fujiwara, M., Mori, K., Rueckert, D., & Glocker, B., Supervoxel classification forests for estimating pairwise image correspondences, Pattern Recognition, Available online 22 September 2016, ISSN 0031-3203

Kanavati, F., Islam, S., Arain, Z., Aboagye, E.O. and Rockall, A., 2020. Fully-automated deep learning slice-based muscle estimation from CT images for sarcopenia assessment. arXiv preprint arXiv:2006.06432.
Kanavati, F., Islam, S., Aboagye, E.O. and Rockall, A., 2018. "Automatic L3 slice detection in 3D CT images using fully-convolutional networks." arXiv preprint arXiv:1811.09244(2018).

Kanavati, F., Misawa, K., Fujiwara, M., Mori, K., Rueckert, D., & Glocker, B. (2017, September). Joint Supervoxel Classification Forest for Weakly-Supervised Organ Segmentation. In International Workshop on Machine Learning in Medical Imaging (pp. 79-87).
Kanavati, F., Tong, T., Misawa, K., Fujiwara, M., Mori, K., Rueckert, D., & Glocker, B. Supervoxel Classification Forests for Estimating Pairwise Image Correspondences. In Machine Learning in Medical Imaging: 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 5, 2015, Proceedings, pp. 94-101. Springer International Publishing, 2015.