Journal of Shandong University(Engineering Science) ›› 2021, Vol. 51 ›› Issue (2): 9-18.doi: 10.6040/j.issn.1672-3961.0.2020.227

• Machine Learning & Data Mining • Previous Articles     Next Articles

MIRGAN: a medical image report generation model based on GAN

Junsan ZHANG1(),Qiaoqiao CHENG1,Yao WAN2,Jie ZHU3,Shidong ZHANG4   

  1. 1. College of Computer Science and Technology, China University of Petroleum(East China), Qingdao 266580, Shandong, China
    2. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, Zhejiang, China
    3. Department of Information Management, the National Police University for Criminal Justice, Baoding 071000, Hebei, China
    4. State Grid Shandong Electric Power Research Institute, Jinan 250003, Shandong, China
  • Received:2020-06-17 Online:2021-04-20 Published:2021-04-16

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Abstract:

The medical image report generation task based on image understanding became a widely concerned issue. Compared with the traditional image understanding task, medical image report generation was a more challenging task. We proposed a medical image report generative adversarial network (MIRGAN) model for this task. A co-attention mechanism was adopted to synthesize the visual and semantic features of multiple feature areas and generate descriptions corresponding to these areas. Combining the generative adversarial networks (GAN) and reinforcement learning (RL) optimized the performance of the generative model to output higher quality reports. The experiment results demonstrated the effectiveness of our proposed MIRGAN model.

Key words: image understanding task, medical image report generation, co-attention mechanism, generative adversarial network, reinforcement learning

CLC Number: 

  • TP391

Fig.1

The whole structure of MIRGAN"

Fig.2

The generative model of MIRGAN"

Fig.3

The discriminative model of MIRGAN"

Table 1

IU X-Ray dataset's introduction"

内容 描述
数据数量 7470
“impression”部分 总体诊断
“findings”部分 局部诊断
“tags”部分 关键字

Table 2

The statistics after preprocessing"

数据 数量
标签 351
字典大小 2195
训练集# 6470
测试集# 500
验证集# 500

Fig.4

The convergence performance of MIRGAN under different training strategies"

Table 3

The evaluation results on the IU X-Ray dataset"

模型 BLEU-1 BLEU-2 BLEU-3 BLEU-4 METEOR ROUGE CIDEr
CNN-RNN[1] 0.316 0.211 0.140 0.095 0.159 0.267 0.111
LRCN[25] 0.369 0.229 0.149 0.099 0.155 0.278 0.190
AdtAtt[10] 0.369 0.226 0.151 0.108 0.171 0.323 0.155
CoAtt[2] 0.382 0.248 0.176 0.125 0.184 0.300 0.287
MIRGAN-visual attention[8] 0.381 0.246 0.171 0.119 0.190 0.327 0.304
MIRGAN-semantic attention[10] 0.372 0.232 0.169 0.120 0.187 0.311 0.298
MIRGAN 0.401 0.257 0.178 0.125 0.194 0.336 0.313

Fig.5

Examples of chest X-ray"

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