Polyp Segmentation Method Combining HarDNet and Reverse Attention

Parameter	m	Stride 2	Stride 4	Stride 8	Stride 16	Stride 32
Value	1.7	3×3，32，stride is 2	8（HDB），k=14，t=128	16（HDB），k=16，t=256	16（HDB），k=40，t=640	4（HDB），k=160，t=1024
Value	1.7	3×3，64	8（HDB），k=14，t=128	16（HDB），k=20，t=320	16（HDB），k=40，t=640	4（HDB），k=160，t=1024

Table 2. Comparison of segmentation effects of different methods on Kvasir SEG and CVC ClinicDB datasets

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Table 2. Comparison of segmentation effects of different methods on Kvasir SEG and CVC ClinicDB datasets

Dataset	Method	mDice	mIoU	$F_{β}^{w}$	$S_{α}$	$E_{ϕ}^{m a x}$	MAE
Kvasir SEG	UNet	0.818	0.746	0.794	0.858	0.893	0.055
	UNet++	0.821	0.743	0.808	0.862	0.910	0.048
	SFA	0.723	0.611	0.670	0.782	0.849	0.075
	PraNet	0.898	0.840	0.885	0.915	0.948	0.030
	HraNet	0.901	0.845	0.890	0.917	0.944	0.027
CVC ClinicDB	UNet	0.823	0.755	0.811	0.889	0.954	0.019
	UNet++	0.794	0.729	0.785	0.873	0.931	0.022
	SFA	0.700	0.607	0.647	0.793	0.885	0.042
	PraNet	0.899	0.849	0.896	0.936	0.979	0.009
	HraNet	0.930	0.879	0.928	0.949	0.980	0.008

Table 3. Generalization capability test results

View table

Table 3. Generalization capability test results

Dataset	Method	mDice	mIoU	$F_{β}^{w}$	$S_{α}$	$E_{ϕ}^{m a x}$	MAE
CVC ColonDB	UNet	0.512	0.444	0.498	0.712	0.776	0.061
	UNet++	0.483	0.410	0.467	0.691	0.760	0.064
	SFA	0.469	0.347	0.379	0.634	0.765	0.094
	PraNet	0.709	0.640	0.696	0.819	0.869	0.045
	HraNet	0.729	0.660	0.720	0.830	0.858	0.038
ETISLarib Polyp DB	UNet	0.398	0.335	0.366	0.684	0.740	0.036
	UNet++	0.401	0.344	0.390	0.683	0.776	0.035
	SFA	0.297	0.217	0.231	0.557	0.633	0.109
	PraNet	0.628	0.567	0.600	0.794	0.841	0.031
	HraNet	0.650	0.581	0.617	0.797	0.827	0.035
Endosece	UNet	0.710	0.627	0.684	0.843	0.876	0.022
	UNet++	0.707	0.624	0.687	0.839	0.898	0.018
	SFA	0.467	0.329	0.341	0.640	0.817	0.065
	PraNet	0.871	0.797	0.843	0.925	0.972	0.010
	HraNet	0.892	0.824	0.875	0.937	0.964	0.007

Table 4. Comparison of training time and reasoning speed of different methods based on same platform
View table
Table 4. Comparison of training time and reasoning speed of different methods based on same platform
Dataset Method Epoch Training /min Inference /（frame·s^-1） mDice mIoU
CVC ClinicDB UNet 50 ~70 ~126 0.823 0.755
UNet++ 50 ~80 ~53 0.794 0.729
SFA 200 >600 ~70 0.700 0.607
PraNet 50 ~75 ~115 0.899 0.849
HraNet 50 ~33 ~148 0.930 0.879

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Ziqi Han, Qiaohong Liu, Chen Ling, Jiawei Liu, Cunjue Liu. Polyp Segmentation Method Combining HarDNet and Reverse Attention[J]. Laser & Optoelectronics Progress, 2023, 60(2): 0217003

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Paper Information

Category: Medical Optics and Biotechnology

Received: Oct. 8, 2021

Accepted: Nov. 22, 2021

Published Online: Jan. 6, 2023

The Author Email: Qiaohong Liu (hqllqh@163.com)

DOI:10.3788/LOP212665

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Detailed implementation parameters of HarDNet68

Table 1. Detailed implementation parameters of HarDNet68

Table 2. Comparison of segmentation effects of different methods on Kvasir SEG and CVC ClinicDB datasets

Table 2. Comparison of segmentation effects of different methods on Kvasir SEG and CVC ClinicDB datasets

Table 3. Generalization capability test results

Table 3. Generalization capability test results

Table 4. Comparison of training time and reasoning speed of different methods based on same platform

Table 4. Comparison of training time and reasoning speed of different methods based on same platform