---
task_categories:
- image-classification
language:
- en
pretty_name: DMR-IR Thermal Breast Cancer Dataset
size_categories:
- 1K<n<10K
tags:
- medical
configs:
- config_name: default
  data_files:
  - split: train
    path: with_mastectomy/train-*
  - split: validation
    path: with_mastectomy/validation-*
  - split: test
    path: with_mastectomy/test-*
dataset_info:
- config_name: default
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': benign
          '1': malignant
  - name: text
    dtype: string
  - name: patient_id
    dtype: string
  - name: text_embedding
    list: float32
  - name: segmentation_mask
    list:
      list:
        list: uint8
  - name: protocol
    dtype: string
  - name: view
    dtype:
      class_label:
        names:
          '0': Frontal
          '1': Right 45°
          '2': Right 90°
          '3': Left 45°
          '4': Left 90°
          '5': Unknown
  - name: record
    dtype: string
  - name: role
    dtype: string
  - name: age_current
    dtype: int64
  - name: age_at_visit
    dtype: int64
  - name: registration_date
    dtype: string
  - name: marital_status
    dtype:
      class_label:
        names:
          '0': Divorced
          '1': Married
          '2': Single
          '3': Widow
  - name: race
    dtype:
      class_label:
        names:
          '0': Asian
          '1': Black
          '2': Indigenous
          '3': Mulatto
          '4': Multiracial
          '5': White
  - name: visit_date
    dtype: string
  - name: complaints
    dtype: string
  - name: symptoms
    dtype: string
  - name: signs
    dtype: string
  - name: last_menstrual_period
    dtype: string
  - name: menopause
    dtype: string
  - name: menarche
    dtype: int64
  - name: eating_habits
    dtype:
      class_label:
        names:
          '0': '35.50'
          '1': High in fat
          '2': Low in fat
          '3': No fat
  - name: cancer_family
    dtype: string
  - name: family_history
    dtype: string
  - name: further_informations
    dtype: string
  - name: mammography
    dtype:
      class_label:
        names:
          '0': 'No'
          '1': 'Yes'
  - name: radiotherapy
    dtype:
      class_label:
        names:
          '0': 'No'
          '1': 'Yes'
  - name: plastic_surgery
    dtype:
      class_label:
        names:
          '0': 'No'
          '1': Yes, both breasts
          '2': Yes, left breast
          '3': Yes, right breast
  - name: prosthesis
    dtype:
      class_label:
        names:
          '0': 'No'
          '1': Yes, both breasts
          '2': Yes, left breast
          '3': Yes, right breast
  - name: biopsy
    dtype:
      class_label:
        names:
          '0': 'No'
          '1': Yes, both breasts
          '2': Yes, left breast
          '3': Yes, right breast
  - name: use_of_hormone_replacement
    dtype:
      class_label:
        names:
          '0': 'No'
          '1': 'Yes'
  - name: nipple_changes
    dtype:
      class_label:
        names:
          '0': Yes, both breasts
          '1': Yes, left breast
          '2': Yes, right breast
  - name: is_there_signal_of_wart_on_breast
    dtype:
      class_label:
        names:
          '0': 'No'
          '1': Yes, both breasts
          '2': Yes, left breast
          '3': Yes, right breast
  - name: medical_further_informations
    dtype: string
  - name: body_temperature
    dtype: float64
  - name: protocol_smoked
    dtype:
      class_label:
        names:
          '0': 'No'
          '1': 'Yes'
  - name: protocol_drank_coffee
    dtype:
      class_label:
        names:
          '0': 'No'
          '1': 'Yes'
  - name: protocol_consumed_alcohol
    dtype:
      class_label:
        names:
          '0': 'No'
  - name: protocol_physical_exercise
    dtype:
      class_label:
        names:
          '0': 'No'
  - name: protocol_put_some_pomade_deodorant_or_products_at_breasts_or_armpits_region
    dtype:
      class_label:
        names:
          '0': 'No'
          '1': 'Yes'
  - name: mastectomy
    dtype:
      class_label:
        names:
          '0': 'No'
          '1': 'Yes'
  splits:
  - name: train
    num_bytes: 6262191255.51803
    num_examples: 4874
  - name: validation
    num_bytes: 1259102362.9086394
    num_examples: 980
  - name: test
    num_bytes: 1315635084.928
    num_examples: 1024
  download_size: 4969929658
  dataset_size: 8836928703.35467
---

# Dataset Card for DMR-IR

DMR-IR is an infrared imaging dataset for Mamma Research, featuring TIFF raw temperature images and clinical metadata with generated text prompts from data acquired at Antônio Pedro University Hospital. It supports multimodal research in breast imaging and diagnostics.

## Dataset Details

### Dataset Description

The DMR-IR dataset is an infrared imaging database for Mamma Research, developed from clinical data acquired at the Antônio Pedro University Hospital. It comprises IR images, digitized mammograms, and associated clinical data from patients in both the screening and gynecologic departments. The IR images were captured using a FLIR SC620 thermal camera with a sensitivity of less than 0.04°C and a capture range of -40°C to 500°C, at a resolution of 640 × 480 pixels. Data acquisition was approved by the Ethical Committee and registered with the Brazilian Ministry of Health (CAAE: 01042812.0.0000.5243). The dataset is accessible through an [online interface.](https://visual.ic.uff.br/dmi/prontuario/home.php)

In our processed version, the original temperature matrices have been converted to TIFF format. Additionally, clinical metadata has been used to generate descriptive text prompts via a custom Python function, summarizing personal, clinical, and protocol information to facilitate multimodal research.

- **Curated by:** Guillermo Pinto, Brayan Quintero, Julian Leon, Miguel Pimiento, Dana Villamizar
- **Shared by [optional]:** The original dataset was shared by Silva, L. F.; Saade, D. C. M.; Sequeiros, G. O.; Silva, A. C.; Paiva, A. C.; Bravo, R. S.; Conci, A.
- **Language(s) (NLP):** English.
- **License:** Not provided – please update if applicable.

### Dataset Sources [optional]

- **Repository:** [DMR-IR Online Interface](http://visual.ic.uff.br/dmi)
- **Paper [optional]:** [DOI: https://doi.org/10.1166/jmihi.2014.1226](https://doi.org/10.1166/jmihi.2014.1226)

## Uses

### Direct Use

This dataset is intended for research in breast imaging and diagnostic analysis, including:
- Developing and testing IR image processing and analysis algorithms.
- Investigating clinical correlations with infrared imaging features.
- Utilizing generated text prompts for multimodal research.

### Out-of-Scope Use

- The dataset should not be used as the sole source for clinical decision making without extensive validation.
- It is not recommended for commercial applications if license restrictions apply.
- Applications outside of its intended research context are not advised.

## Dataset Creation

### Curation Rationale

The dataset was curated to improve the usability of the original IR images by converting them from temperature matrices to TIFF format, enhancing reproducibility. In addition, the integration of descriptive clinical metadata allows the clinical context to be incorporated into image analysis workflows. Furthermore, the dataset is not locked, meaning its contents may evolve over time. For research purposes—particularly when the dataset is used to conduct a study—it is necessary to work with a locked version to ensure reproducibility and consistency of the findings. Additionally, some data anomalies were corrected following the recommendations presented in [https://doi.org/10.1177/14604582231153779](https://doi.org/10.1177/14604582231153779).

### Source Data

The original data was collected at Antônio Pedro University Hospital and comprises:
- Infrared images captured with a FLIR SC620 thermal camera.
- Digitized mammograms.
- Clinical data from both healthy patients and patients with breast diseases.

#### Data Collection and Processing

- **Original Collection:** Data was acquired from patients in the screening and gynecologic departments following ethical guidelines approved by the hospital’s Ethical Committee and registered under CAAE: 01042812.0.0000.5243.

### Annotations [optional]

#### Annotation process

The dataset does not include manual annotations beyond the automated generation of clinical text prompts based on existing metadata. **Only the `mastectomy` column was manually annotated**, while the remaining metadata fields were cleaned and corrected. The full pipeline to reproduce the dataset creation is available at [https://github.com/semilleroCV/BreastCATT/tree/main/data/DMR-IR](https://github.com/semilleroCV/BreastCATT/tree/main/data/DMR-IR).

#### Personal and Sensitive Information

The clinical metadata includes sensitive personal and medical information such as risk factors (e.g., eating habits, family history, radiotherapy, hormone replacement, age, menarche/menopause), complementary features (e.g., prosthesis, nipple changes), and protocol details (e.g., smoking, alcohol, coffee consumption). Although explicit identifiers are not provided, the data is sensitive and should be handled in accordance with applicable privacy regulations.  

## Bias, Risks, and Limitations

- The dataset reflects the clinical demographics and practices of a specific hospital in Brazil, which may limit its generalizability to other populations.
- The calibration process and text prompt generation depend on the accuracy of the thermal parser tool and the quality of the clinical metadata.
- The selection of clinical features may introduce bias that could affect downstream analysis.

## Additional Information

### Citation Information

**Dataset paper**

```bibtex
@article{silva2014new,
  title={A new database for breast research with infrared image},
  author={Silva, LF and Saade, DCM and Sequeiros, GO and Silva, AC and Paiva, AC and Bravo, R de S and Conci, Aura},
  journal={Journal of Medical Imaging and Health Informatics},
  volume={4},
  number={1},
  pages={92--100},
  year={2014},
  publisher={American Scientific Publishers}
}
```

**Our research paper**

```bibtex
@INPROCEEDINGS{pintobreastcatt,
  author={Pinto, Guillermo and León, Julián and Quintero, Brayan and Villamizar, Dana and Rueda-Chacón, Hoover},
  booktitle={2025 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)}, 
  title={Multimodal Vision-Language Transformer for Thermography Breast Cancer Classification}, 
  year={2025},
  volume={},
  number={},
  pages={1-6},
  keywords={Sensitivity;Translation;Mortality;Infrared imaging;Medical services;Metadata;Transformers;Breast cancer;Standards;Periodic structures;Breast cancer;deep learning;thermography;vision-language transformer;clinical metadata;cross-attention},
  doi={10.1109/ColCACI67437.2025.11230909}}
```