Source-linked AI summary
On the Automatic Generation of Medical Imaging Reports
Baoyu Jing, Pengtao Xie, Eric Xing
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.
Method
The model jointly predicts tags and generates descriptions, uses co-attention over visual regions and predicted tags, and employs hierarchical LSTM generation.
Results
The authors report effective proposed methods in extensive experiments and describe visual and semantic attention behaviors in generated reports.
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.
Takeaways & Limitations
Failure cases include incorrect major-abnormality judgments, imprecise descriptions, and false abnormalities, with darker images identified as a possible sensitivity factor.
Abstract
from arXiv · showhide
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.
- Motivation: 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.
- 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.
- 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.
- Proposed approach: 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.
- 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.
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.
- 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.
- 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.
- 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.
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.
- 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.
- 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.
- 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.
- 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.
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.
- 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.
- 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.
- 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.
- 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.
- 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.
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.
- Co-attention combines visual and semantic information to localize abnormal regions and describe them accurately.
- A hierarchical LSTM captures long-range semantics and produces high-quality reports containing multiple sentences or paragraphs.
- The methods are evaluated on two medical datasets containing radiology and pathology images using quantitative and qualitative studies.