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On the Automatic Generation of Medical Imaging Reports

Baoyu Jing, Pengtao Xie, Eric Xing

arXiv:1711.08195v3cs.CLcs.CV

TL;DR

The paper asks whether medical-image reports can be generated automatically to reduce demanding or tedious report-writing work. It jointly predicts tags and generates descriptions using co-attention and hierarchical LSTM components, and reports effective performance in experiments on two publicly available datasets. The authors also show failure cases involving incorrect abnormality judgments and mis-descriptions.

  • Problem

    Medical-image report generation must handle heterogeneous tags and text, localize abnormal regions, and produce long multi-sentence reports, while manual reporting is demanding or tedious.

    Source: §1 · pp. 1–2

  • Method

    The model jointly predicts tags and generates descriptions, uses co-attention over visual regions and predicted tags, and employs hierarchical LSTM generation.

    Source: §1 · p. 2, §5 · p. 9

  • Results

    The authors report effective proposed methods in extensive experiments and describe visual and semantic attention behaviors in generated reports.

    Source: §1 · p. 2, §4.5.2 · p. 9, §5 · p. 9

  • Takeaways & Limitations

    The paper demonstrates a unified approach for predicting report tags and generating long medical-image descriptions while connecting descriptions to image regions and semantic tags.

    Source: §1 · p. 2, §5 · p. 9

    Failure cases include incorrect major-abnormality judgments, imprecise descriptions, and false abnormalities, with darker images identified as a possible sensitivity factor.

    Source: §4.5.1 · pp. 7–8

Abstract

from arXiv · show

Medical imaging is widely used in clinical practice for diagnosis and treatment. Report-writing can be error-prone for unexperienced physicians, and time- consuming and tedious for experienced physicians. To address these issues, we study the automatic generation of medical imaging reports. This task presents several challenges. First, a complete report contains multiple heterogeneous forms of information, including findings and tags. Second, abnormal regions in medical images are difficult to identify. Third, the re- ports are typically long, containing multiple sentences. To cope with these challenges, we (1) build a multi-task learning framework which jointly performs the pre- diction of tags and the generation of para- graphs, (2) propose a co-attention mechanism to localize regions containing abnormalities and generate narrations for them, (3) develop a hierarchical LSTM model to generate long paragraphs. We demonstrate the effectiveness of the proposed methods on two publicly available datasets.

1 Introduction

The paper targets automatic medical-image report generation because reports are difficult for less-experienced clinicians and tedious for experienced ones. It addresses heterogeneous report content, abnormal-region localization, and long-paragraph generation with a unified multi-task, co-attentive hierarchical model.

Motivation

  • Report writing demands extensive diagnostic knowledge from less-experienced radiologists and pathologists.Required skills include anatomical and disease knowledge, systematic image analysis, temporal evaluation, and correlation with clinical and diagnostic information.Source: §1 · p. 1
  • Experienced radiologists may read hundreds of images daily, with each report taking about 5-10 minutes to type.The authors describe report writing as time-consuming and unpleasant for both inexperienced and experienced professionals.Source: §1 · pp. 1–2

Challenges

  • Complete diagnostic reports combine heterogeneous outputs: an impression sentence, findings paragraph, and keyword tags.The framework treats tag prediction as multi-label classification and long-description generation as text generation.Source: §1 · pp. 1–2, Figure 1 · p. 1

Proposed approach

  • The proposed co-attention mechanism jointly attends to image regions and predicted tags to localize abnormalities and generate corresponding descriptions.It explores visual and semantic information together when associating regions with descriptions.Source: §1 · p. 2
  • A hierarchical LSTM generates long reports by first producing high-level topics and then fine-grained descriptions.This hierarchical design follows the report’s multi-sentence compositional structure rather than relying on a single-layer decoder.Source: §1 · p. 2

Contributions

  • The paper combines multi-task learning, co-attention, and hierarchical LSTM modeling, and evaluates the methods through extensive experiments.The multi-task framework jointly predicts tags and generates text descriptions.Source: §1 · p. 2

2 Related Works

Prior work labeled medical images with structured outputs and generated captions using CNN-RNN and attention-based models. This paper extends those directions toward natural, long physician reports by combining visual-semantic co-attention with hierarchical paragraph generation.

Medical-image labeling

  • Earlier medical-image labeling methods predicted attributes, tags, or semi-structured pathology reports rather than natural physician-written reports.The authors argue that natural reports better reflect varying physician writing habits and image-specific abnormalities.Source: §2 · p. 2

Image captioning

  • CNN-RNN frameworks became standard for image captioning, while later methods added spatial-visual or semantic attention.The proposed model combines both visual features and semantic tags through co-attention.Source: §2 · p. 2

Position of this work

  • The proposed model addresses reports containing both long text descriptions and lists of tags.Its multi-task hierarchical design predicts keywords while generating long paragraphs from image regions and tag representations.Source: Figure 2 · p. 3

Paragraph generation

  • Hierarchical LSTMs were previously used for paragraph captions, but this method uses a co-attention network to generate topics.The distinction is the integration of topic generation with visual and semantic attention for medical-report generation.Source: §2 · p. 3, Figure 2 · p. 3

3 Methods

The model jointly predicts image tags and generates long diagnostic paragraphs using co-attention and hierarchical LSTMs. Visual regions and semantic tags are combined to generate sentence topics and their words sequentially.

Tag prediction

  • The model uses CNN visual features and a multi-label classification network to predict relevant image tags.Each tag is represented by a word-embedding vector and the most likely tags provide semantic features for report generation.Source: §3.1 · p. 3, §3.2 · p. 3

Hierarchical paragraph generation

  • The word LSTM receives each topic vector and generates the corresponding sentence word by word.After all word-LSTM sequences are generated, the final report is formed by concatenating the sentences.Source: §3.1 · p. 3, §3.5 · p. 4

Co-attention

  • Co-attention simultaneously attends to image features and predicted semantic tags to form a joint context vector.The visual and semantic context vectors are concatenated and transformed by a fully connected layer before topic generation.Source: §3.3 · p. 4

Hierarchical paragraph generation

  • The sentence LSTM converts joint context into topic vectors and predicts whether to continue or stop generating captions.A topic vector represents one sentence’s semantics, while the stop-control component determines when topic generation ends.Source: §3.4 · p. 4

Parameter learning

  • Training combines tag-classification, sentence-stop, and word-generation cross-entropy losses with attention regularization.The attention regularizer encourages equal attention across image regions and tags.Source: §3.6 · p. 5

4 Experiments

Experiments on two public medical-image datasets evaluate paragraph and single-sentence generation, showing benefits from hierarchical decoding and co-attention. Qualitative analyses further examine abnormality detection, normality coverage, and attention behavior.

Datasets

  • The study evaluates its model on two publicly available datasets: IU X-Ray for paragraphs and PEIR Gross for single-sentence captions.IU X-Ray contains 7,470 image-report pairs, while PEIR Gross contains 7,442 image-caption pairs from 21 sub-categories.Source: §4.1 · p. 5

Paragraph Generation

  • Models with hierarchical LSTM decoders perform much better than single-LSTM models for paragraph generation.The comparison between Ours-no-Attention and CNN-RNN isolates the decoder difference and directly demonstrates the effectiveness of hierarchical LSTM.Source: §4.4 · p. 6, §4.3 · p. 6

Ablation Analysis

  • Using only semantic or visual attention provides limited help for paragraph generation because each attention type misses complementary information.Visual attention may miss whole-image semantics, whereas semantic attention is inadequate for localizing small abnormal regions.Source: §4.4 · pp. 6–7

Quantitative Results

  • Ours-CoAttention achieves the best results on all evaluation metrics for paragraph generation and outperforms all baselines for single-sentence generation.The evaluation reports BLEU, METEOR, ROUGE, and CIDER scores for both tasks.Source: §4.4 · p. 7

Qualitative Results

  • Qualitative examples show that co-attention more accurately captures abnormalities, while darker images can produce imprecise descriptions and false abnormality detections.One failure case includes incorrect major-abnormality judgment despite finding some unusual regions.Source: §4.5.1 · pp. 7–8

Co-Attention Learning

  • Co-attention visualizations show sentence-specific attention to image regions and tags, while incorrect semantic attention can cause mis-descriptions.Attention focuses near the heart for a cardio-related sentence and on “degenerative change” for another example.Source: §4.5.2 · pp. 8–9

5 Conclusion

The paper addresses three challenges in automatic medical-image report generation with unified multi-task learning, co-attention, and hierarchical LSTM methods. Effectiveness is demonstrated through quantitative and qualitative studies on two medical datasets.

  • The framework jointly predicts tags and generates descriptions to handle heterogeneous report information within one model.Source: §5 · p. 9
  • Co-attention combines visual and semantic information to localize abnormal regions and describe them accurately.Source: §5 · p. 9
  • A hierarchical LSTM captures long-range semantics and produces high-quality reports containing multiple sentences or paragraphs.Source: §5 · p. 9
  • The methods are evaluated on two medical datasets containing radiology and pathology images using quantitative and qualitative studies.Source: §5 · p. 9

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