@inproceedings{64ceab9818f147ccb74e035ae058c6bc,
title = "A 2.5D deep learning-based approach for prostate cancer detection on T2-weighted magnetic resonance imaging",
abstract = "In this paper, we propose a fully automatic magnetic resonance image (MRI)-based computer aided diagnosis (CAD) system which simultaneously performs both prostate segmentation and prostate cancer diagnosis. The system utilizes a deep-learning approach to extract high-level features from raw T2-weighted MR volumes. Features are then remapped to the original input to assign a predicted label to each pixel. In the same context, we propose a 2.5D approach which exploits 3D spatial information without a compromise in computational cost. The system is evaluated on a public dataset. Preliminary results demonstrate that our approach outperforms current state-of-the-art in both prostate segmentation and cancer diagnosis.",
author = "Ruba Alkadi and Ayman El-Baz and Fatma Taher and Naoufel Werghi",
note = "Funding Information: Acknowledgement. This work is supported by a research grant from Al-Jalila foundation Ref: AJF-201616. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 15th European Conference on Computer Vision, ECCV 2018 ; Conference date: 08-09-2018 Through 14-09-2018",
year = "2019",
doi = "10.1007/978-3-030-11018-5_66",
language = "British English",
isbn = "9783030110178",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "734--739",
editor = "Laura Leal-Taix{\'e} and Stefan Roth",
booktitle = "Computer Vision – ECCV 2018 Workshops, Proceedings",
address = "Germany",
}
