Dual Parallel Reverse Attention Edge Network : DPRA-EdgeNet

TY - JOUR

T1 - Dual Parallel Reverse Attention Edge Network : DPRA-EdgeNet

AU - Bhattacharya, Debayan

AU - Betz, Christian Stephan

AU - Eggert, Dennis

AU - Schlaefer, Alexander

PY - 2021/11/1

Y1 - 2021/11/1

N2 - In this paper, we propose Dual Parallel Reverse Attention Edge Network (DPRA-EdgeNet), an architecture that jointly learns to segment an object and its edge. Specifically, the model uses two cascaded partial decoders to form two initial estimates of the object segmentation map and its corresponding edge map. This is followed by a series of object decoders and edge decoders which work in conjunction with dual parallel reverse attention modules. The dual parallel reverse attention (DPRA) modules repeatedly prunes the features at multiple scales to put emphasis on the object segmentation and the edge segmentation respectively. Furthermore, we propose a novel decoder block that uses spatial and channel attention to combine features from the preceding decoder block and reverse attention (RA) modules for object and edge segmentation. We compare our model against popular segmentation models such as U-Net, SegNet and PraNet and demonstrate through a five fold cross validation experiment that our model improves the segmentation accuracy significantly on the Kvasir-SEG dataset and Kvasir-Instrument dataset.

AB - In this paper, we propose Dual Parallel Reverse Attention Edge Network (DPRA-EdgeNet), an architecture that jointly learns to segment an object and its edge. Specifically, the model uses two cascaded partial decoders to form two initial estimates of the object segmentation map and its corresponding edge map. This is followed by a series of object decoders and edge decoders which work in conjunction with dual parallel reverse attention modules. The dual parallel reverse attention (DPRA) modules repeatedly prunes the features at multiple scales to put emphasis on the object segmentation and the edge segmentation respectively. Furthermore, we propose a novel decoder block that uses spatial and channel attention to combine features from the preceding decoder block and reverse attention (RA) modules for object and edge segmentation. We compare our model against popular segmentation models such as U-Net, SegNet and PraNet and demonstrate through a five fold cross validation experiment that our model improves the segmentation accuracy significantly on the Kvasir-SEG dataset and Kvasir-Instrument dataset.

U2 - 10.5617/nmi.9116

DO - 10.5617/nmi.9116

M3 - SCORING: Journal article

VL - 2021

SP - 11

EP - 13

JO - Nordic Machine Intelligence, MedAI

JF - Nordic Machine Intelligence, MedAI

SN - 2703-9196

IS - 1

M1 - Vol. 1 No. 1

ER -