【论文笔记】Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation (AAAI 2019)

论文原文：https://arxiv.org/pdf/1903.10122.pdf

Abstract

• Knowledge-driven Encode, Retrieve, Paraphrase (KERP) approach 知识驱动的编码、检索、释义(KERP)方法
• decomposes medical report generation into explicit medical abnormality graph learning 显式医学异常图学习 and subsequent natural language modeling 自然语言建模

visual features --(Encode Module)–> an abnormality graph --(Retireve module)–> sequences of templates --(Paraphrase module)–> sequences of words

• generates structured and robust reports supported with accurate abnormality prediction 生成结构化和健壮的报告，支持准确的异常预测
• produces explainable attentive regions which is crucial for interpretative diagnosis 产生可解释的注意区域，这对解释性诊断至关重要

GTR

• core of KERP

• dynamically transforms high-level semantics between graph-structured data of multiple domains such as knowledge graphs, images and sequences 在知识图、图像和序列等多个领域的图结构数据之间动态转换高级语义

GTR as a module

1. concatenating intra-graph message passing and inter-graph message passing into one step 将图内消息传递和图间消息传递连接到一个步骤中
   1. conduct message passing within target graph 在目标图内进行消息传递
   2. conduct message passing from one / multiple source graph 从一个/多个源图传递消息
2. stacking multiple such steps into one module 将多个这样的步骤叠加到一个模块中
   • convert target graph features into high-level semantics 将目标图特性转换为高级语义

GTR as a multiple domains

• ${\text{GTR}}_{\text{i2g}}$: image features --> graph’s features
• ${\text{GTR}}_{\text{g2s}}$: input - graph; output - sequence
• ${\text{GTR}}_{\text{g2g}}$: a graph --> another graph
  • abnormality graph --> disease graph
• ${\text{GTR}}_{\text{gs2s}}$: input - graph&sequence; output - sequence

GTR for sequential input/output

positional encoding – relative and absolute position information 位置编码——相对和绝对位置信息

KERP

Encode module

• transforms visual features into a structured abnormality graph by incorporating prior medical knowledge 将视觉特征转化为结构化的异常图
• each node represents a possible clinical abnormality 临床异常

updated node features:
$$\begin{gathered} {\text{h}}_u=GT{R}_{i2g}(\text{X}) \\ u=sigmoid({\text{W}}_u{\text{h}}_u) \end{gathered}$$

• ${\text{W}}_u$: linear projection to transform latent feature u into 1-d probability 线性投影将潜在特征u转化为一维概率
• ${\text{h}}_u=({\text{h}}_{u_1};{\text{h}}_{u_2};...;{\text{h}}_{u_N})\in R^{N,d}$: the set of latent features of nodes where d is feature dimension 节点潜在特征集，其中d为特征维数
• u = ( u 1 , u 2 , . . . , u N ) , y i ∈ { 0 , 1 } , i ∈ { 1 , . . . , N } \text{u}=(u_1,u_2,...,u_N),y_i\in \{0,1\}, i\in\{1,...,N\} u=(u1​,u2​,...,uN​),yi​∈{ 0,1},i∈{ 1,...,N}:binary label for abnormality nodes 异常节点的二进制标签

Retrieve module 检索

• retrieves text templates based on the detected abnormalities 根据检测到的异常检索文本模板

obtain template sequence:
$$\begin{gathered} {\text{h}}_t=GT{R}_{g2s}({\text{h}}_u) \\ t=\text{argmax}Softmax({\text{W}}_t{\text{h}}_t) \end{gathered}$$

• ${\text{W}}_t$: linear projection to transform latent feature to template embedding 线性投影 将潜在特征转化为模板嵌入

Paraphrase module

• refine templates with enriched details and possibly new case-specific findings 用丰富的细节和可能的新的特定病例发现来改进模板
  • by modifying information in the templates that is not accurate for specific cases 通过修改模板中对于特定情况不准确的信息
• convert templates into more natural and dynamic expressions 将模板转换为更自然和生动的表达式
  • by robust language modeling for the same content通过对同一内容进行稳健的语言建模

$$\begin{gathered} {\text{h}}_w=GT{R}_{gs2s}({\text{h}}_u,t) \\ R=\text{argmax}Softmax({\text{W}}_{w}f({\text{h}}_w)) \end{gathered}$$

• $f$: the operation of reshaping ${\text{h}}_w$ from $R^{N_s,N_w,d}$ to $R^{N_s\ast N_w,d}$
• ${\text{W}}_w$: linear projection to transform latent feature into word embedding 线性投影 将潜在特征转化为文字嵌入

Disease classification

multi-label disease classification: 多标记疾病分类
$$\begin{gathered} {\text{h}}_z=GT{R}_{g2g}({\text{h}}_u) \\ z=sigmoid({\text{W}}_z{\text{h}}_z) \end{gathered}$$
${\text{W}}_z$: linear projection to transform disease nodes feature into 1-d probability 线性投影 将疾病节点特征转化为一维概率

Learning

During paraphrasing, the retrieved templates t, instead of latent feature ${\text{h}}_t$, is used for rewriting. Sampling the templates of maximum predicted probability breaks the connectivity of differentiable back-propagation of the whole encode retrieve-paraphrase pipeline. 破坏了整个编码-检索-转述管道的可微调反向传播的连接性

1. train the Paraphrase with ground truth templates
2. then with **sampled templates **采样模板 generated by Retrieval module

Results

Conclusion

• accurate attributes prediction
• dynamic medical knowledge graph
• explainable location reference 可解释的位置参考