Task 1

Task 1 focuses on generating a causality exploration section for the diagnosis in radiology report. Training set contains the relevant index to the Chest X-ray radiology reports and their DICOM images from the MIMIC-CXR dataset [1] with their corresponding causality exploration sections that are collected by the radiologists under the simulated diagnostic processes for the selected data from MIMIC-CXR dataset. The image input (DICOM) is optional, allowing participants to decide whether to include the image data as part of their analysis. Additionally, participants may choose to incorporate their own knowledge bases, ontologies, or other external resources as optional inputs, further enhancing their causality analysis with additional context and domain-specific insights.

Task 1 Dataset:  

Input

• • • • (Text) MIMIC CXR Reports  (MIMIC report) 

      • (Option, Image) MIMIC Chest X-ray image

      • (Option, Additional data available) 

Output

• (Text) Causal exploration report

Example of Task1 training set (Input, Output)

Task definition

The objective of this task is to develop a model that learns to map a set of input data—consisting of radiology reports, optionally accompanied by chest X-ray images—to a corresponding set of output data that includes a causality exploration section. This section would not typically be described in the initial radiology report but has been recovered and verified by radiology experts based on a structured diagnosis confirmation checklist.

The input data for this task is sourced from the MIMIC database [1], a large, publicly available database of healthcare information. Participants must individually acquire the necessary licensing and permissions to access MIMIC data. The task organizers will provide the access method to the relevant data for participants who hold a valid MIMIC license, enabling them to retrieve and utilize the input data in line with MIMIC’s licensing requirements.

Participants will use the provided training set, which includes paired examples of input data (radiology report and optional X-ray image) and output data (causality exploration section), to train a learning module. This module should capture the underlying patterns and infer causal information that experts derive from both the report content and the diagnosis confirmation process.

Participants are required to build a running module (also referred to as the inference module) that can take any new input (a radiology report with or without an X-ray image) and generate the corresponding causality exploration section based on the learned transformation. This module will be deployed and tested on our evaluation server via API to assess its accuracy and effectiveness on testing inputs that were not included in the training set.

Output

The output of Task 1 is a causality exploration report. This report should provide a structured analysis of the radiology findings, highlighting potential causative relationships that could lead to a better understanding of the patient's condition. The report should reflect the diagnostic reasoning process by documenting how various symptoms and findings may be interlinked. For example, a finding of "pleural effusion" may be linked causally to "heart failure" if observed in the patient's medical history.

The report must begin with the fixed heading "Causal Exploration:" followed by the causality analysis text that reflects the diagnostic flow and reasoning. This structured format is required for consistency. The output format should clearly delineate identified causal links and any inferred reasoning steps that mimic a radiologist’s analytical process.

References

[1] Johnson, A., Pollard, T., Mark, R., Berkowitz, S., & Horng, S. (2024). MIMIC-CXR Database (version 2.1.0). PhysioNet. https://doi.org/10.13026/4jqj-jw95.

[2] Johnson, A.E.W., Pollard, T.J., Berkowitz, S.J., et al. (2019). MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Scientific Data, 6, 317. https://doi.org/10.1038/s41597-019-0322-0.

[3] Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101 (23), pp. e215–e220.