| --- |
| license: cc-by-4.0 |
| configs: |
| - config_name: PA-01_Cervical_Spine |
| data_files: |
| - split: train |
| path: PA-01_Cervical_Spine/train-* |
| - config_name: PA-02_Whole_Spine |
| data_files: |
| - split: train |
| path: PA-02_Whole_Spine/train-* |
| - config_name: PA-03_Brain |
| data_files: |
| - split: train |
| path: PA-03_Brain/train-* |
| - config_name: PA-04_Pelvic |
| data_files: |
| - split: train |
| path: PA-04_Pelvic/train-* |
| - config_name: PA-05_Right Shoulder_joint |
| data_files: |
| - split: train |
| path: PA-05_Right Shoulder_joint/train-* |
| dataset_info: |
| - config_name: PA-01_Cervical_Spine |
| features: |
| - name: patient_id |
| dtype: string |
| - name: image |
| dtype: image |
| - name: slice_index |
| dtype: int64 |
| - name: modality |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 3547733 |
| num_examples: 48 |
| download_size: 3549459 |
| dataset_size: 3547733 |
| - config_name: PA-02_Whole_Spine |
| features: |
| - name: patient_id |
| dtype: string |
| - name: image |
| dtype: image |
| - name: slice_index |
| dtype: int64 |
| - name: modality |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 11712708 |
| num_examples: 96 |
| download_size: 11714311 |
| dataset_size: 11712708 |
| - config_name: PA-03_Brain |
| features: |
| - name: patient_id |
| dtype: string |
| - name: image |
| dtype: image |
| - name: slice_index |
| dtype: int64 |
| - name: modality |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 7835307 |
| num_examples: 148 |
| download_size: 7835138 |
| dataset_size: 7835307 |
| - config_name: PA-04_Pelvic |
| features: |
| - name: patient_id |
| dtype: string |
| - name: image |
| dtype: image |
| - name: slice_index |
| dtype: int64 |
| - name: modality |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 14135319 |
| num_examples: 165 |
| download_size: 14133433 |
| dataset_size: 14135319 |
| - config_name: PA-05_Right Shoulder_joint |
| features: |
| - name: patient_id |
| dtype: string |
| - name: image |
| dtype: image |
| - name: slice_index |
| dtype: int64 |
| - name: modality |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 6050396 |
| num_examples: 131 |
| download_size: 6050211 |
| dataset_size: 6050396 |
| tags: |
| - mri-reports |
| - medical-reports |
| - radiology-reports |
| - medical-nlp |
| - healthcare-ai |
| - biomedical-nlp |
| - clinical-text |
| - medical-imaging |
| - document-understanding |
| - information-extraction |
| size_categories: |
| - 100K<n<1M |
| task_categories: |
| - image-classification |
| --- |
| **Dataset Description:** |
|
|
| **This dataset is a large-scale collection of MRI (Magnetic Resonance Imaging) reports with no findings, containing data from 13,956 patients and 3,079,197 medical images, designed to support the development and training of advanced healthcare AI, medical imaging, and diagnostic AI systems.** |
|
|
| It consists of MRI data where the radiological reports do not include any specific findings. These cases are useful for building AI systems for training, pretraining, and model validation workflows. |
| The dataset captures real-world imaging characteristics such as scanner variability, acquisition protocols, and patient positioning, while ensuring that all included samples have reports with no findings recorded. |
| Additionally, this dataset can be used in pipelines for Supervised Fine-Tuning (SFT) and Self-Supervised Learning (SSL) workflows. |
|
|
| **Dataset Specification** |
|
|
| -Patients: 13,956 |
| -Images: 3,079,197 |
| -Modality: MRI (Magnetic Resonance Imaging) |
| -Type: Medical images with no findings |
| -Data Source: Clinical MRI reports |
| -Body Regions: Brain, Spine, Abdomen, etc. |
| -Data Nature: Real-world clinical data |
| |
|
|
| **Key Use Cases** |
|
|
| -Baseline model training for MRI image analysis |
| -Anomaly detection pretraining |
| -Reducing model bias in medical AI |
| -Medical imaging benchmarking |
| -Quality control and validation systems |
| -Clinical AI calibration |
| |
|
|
|
|
| **Value of This Dataset** |
|
|
| -Provides high-quality MRI data with no findings |
| -Supports model pretraining and evaluation |
| -Useful for training robust diagnostic AI |
| -Facilitates unbiased learning for anomaly detection |
| -Helps in clinical validation workflows |
| |
|
|
| **Quality Analysis** |
|
|
| | Metric | Best Dataset Result | Importance | |
| | --------------------------------- | -------------------------- | --------------------------------------------------------------------- | |
| | **Resolution** | **398×398 avg to 576×576** | Preserves high anatomical and structural detail for accurate analysis | |
| | **SNR (Signal-to-Noise Ratio)** | **25.67** | Indicates strong signal quality with lower noise interference | |
| | **CNR (Contrast-to-Noise Ratio)** | **50.37** | Shows excellent contrast and clear tissue separation | |
| | **Blur Score (Sharpness)** | **363.55** | Reflects extremely sharp and well-defined image quality | |
|
|
|
|
| **Basic JSON Schema** |
| ```json |
| { |
| "patient_id": "string", |
| "image": "Image", |
| "slice_index": "int64", |
| "modality": "string" |
| } |
| ``` |
| **Data Creation** |
|
|
| Procured through formal agreements and generated in the ordinary course of business. |
|
|
| **Considerations** |
|
|
| This dataset is provided for research and educational purposes only. It contains only sample data. For access to the full dataset and enterprise licensing options, please visit our website [InfoBay.AI](https://infobay.ai/) or contact us directly. |
|
|
| -Ph: (91) 8303174762 |
| -Email: datareq@infobay.ai |
