TY  - INPR
JF  - IEEE Journal of Biomedical and Health Informatics
PB  - Institute of Electrical and Electronics Engineers (IEEE)
A1  - Lee, Matthew
A1  - Sanchez-Matilla, Ricardo
A1  - Stoyanov, Danail
A1  - Luengo, Imanol
KW  - Neural Architecture Search
KW  -  Surgical Image Segmentation
KW  -  Surgical Keypoint Detection
KW  -  Surgical
Instrument Detection
SP  - 1
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ID  - discovery10193741
UR  - http://dx.doi.org/10.1109/jbhi.2024.3406065
EP  - 13
N2  - Deep learning has been used across a large number of computer vision tasks, however designing the network architectures for each task is time consuming. Neural Architecture Search (NAS) promises to automatically build neural networks, optimised for the given task and dataset. However, most NAS methods are constrained to a specific macro-architecture design which makes it hard to apply to different tasks (classification, detection, segmentation). Following the work in Differentiable NAS (DNAS), we present a simple and efficient NAS method, Differentiable Parallel Operation (DIPO), that constructs a local search space in the form of a DIPO block, and can easily be applied to any convolutional network by injecting it in-place of the convolutions. The DIPO block's internal architecture and parameters are automatically optimised end-to-end for each task. We demonstrate the flexibility of our approach by applying DIPO to 4 model architectures (U-Net, HRNET, KAPAO and YOLOX) across different surgical tasks (surgical scene segmentation, surgical instrument detection, and surgical instrument pose estimation) and evaluated across 5 datasets. Results show significant improvements in surgical scene segmentation (+10.5% in CholecSeg8K, +13.2% in CaDIS), instrument detection (+1.5% in ROBUST-MIS, +5.3% in RoboKP), and instrument pose estimation (+9.8% in RoboKP).
AV  - public
SN  - 2168-2194
Y1  - 2024/05/28/
TI  - DIPO: Differentiable Parallel Operation Blocks for Surgical Neural Architecture Search
ER  -