TY  - GEN
AV  - public
EP  - 305
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ID  - discovery10114066
CY  - Cham, Switzerland
T3  - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB  - Springer
A1  - Puyal, JGB
A1  - Bhatia, KK
A1  - Brandao, P
A1  - Ahmad, OF
A1  - Toth, D
A1  - Kader, R
A1  - Lovat, L
A1  - Mountney, P
A1  - Stoyanov, D
Y1  - 2020/09/29/
N2  - Colonoscopy is the gold standard for early diagnosis and pre-emptive treatment of colorectal cancer by detecting and removing colonic polyps. Deep learning approaches to polyp detection have shown potential for enhancing polyp detection rates. However, the majority of these systems are developed and evaluated on static images from colonoscopies, whilst applied treatment is performed on a real-time video feed. Non-curated video data includes a high proportion of low-quality frames in comparison to selected images but also embeds temporal information that can be used for more stable predictions. To exploit this, a hybrid 2D/3D convolutional neural network architecture is presented. The network is used to improve polyp detection by encompassing spatial and temporal correlation of the predictions while preserving real-time detections. Extensive experiments show that the hybrid method outperforms a 2D baseline. The proposed architecture is validated on videos from 46 patients. The results show that real-world clinical implementations of automated polyp detection can benefit from the hybrid algorithm.
TI  - Endoscopic Polyp Segmentation Using a Hybrid 2D/3D CNN
KW  - Colonoscopy
KW  -  Polyp detection
KW  -  Computer aided diagnosis
SP  - 295
UR  - https://doi.org/10.1007/978-3-030-59725-2_29
ER  -