---
license: apache-2.0
task_categories:
  - image-segmentation
  - object-detection
tags:
  - medical
  - chest-x-ray
  - radiograph
  - thorax
  - lesion-segmentation
  - multi-label
pretty_name: ChestX-Det (13-class thoracic abnormality segmentation)
size_categories:
  - 1K<n<10K
---

# ChestX-Det

**3,578 frontal chest radiographs** from NIH ChestX-ray14, annotated by three
board-certified radiologists with **13 categories** of thoracic abnormality —
each as a per-instance **polygon contour** plus a bounding box.

- **Modality:** chest X-ray (2D frontal radiograph), 1024×1024, 8-bit
- **Organ:** thorax — lung, pleura, mediastinum, ribs, heart
- **Targets:** 13 abnormality classes, as independent binary masks
- **License:** **Apache-2.0** (annotations) over NIH ChestX-ray14 images (no use restrictions, attribution required)
- **Source:** [Deepwise-AILab/ChestX-Det-Dataset](https://github.com/Deepwise-AILab/ChestX-Det-Dataset) (official)

## ⚠️ ChestX-Det vs ChestX-Det10 — only one is a segmentation dataset

|  | **ChestX-Det** (this repo) | ChestX-Det10 |
|---|---|---|
| Images | **3,578** | 3,543 (exact subset) |
| Classes | **13** | 10 |
| `polygons` key | **present in all 3,578** | **absent in all 3,543** |
| License | **Apache-2.0** | none — all rights reserved |

ChestX-Det10 is **boxes-only** and cannot be used for segmentation. Much of the
literature writes "ChestX-Det" when it means Det10 — check for the `polygons`
key. ChestX-Det adds 3 classes (Cardiomegaly, Diffuse Nodule, Pleural
Thickening), 35 images, and the polygon contours.

## Splits

Both official. There is no validation split.

| Split | Images | Instances | No-finding images |
|---|---|---|---|
| `train` | 3,025 | 7,905 | 547 |
| `test` | 553 | 1,734 | 64 |
| **Total** | **3,578** | **9,639** | **611 (17.1%)** |

## Classes

Counts are **(image, class) pairs** — i.e. images on which that class appears —
followed by instance counts.

| Class | Column prefix | Images | Instances |
|---|---|---|---|
| Consolidation | `consolidation` | 1,642 | 2,563 |
| Effusion | `effusion` | 1,441 | 2,112 |
| Fibrosis | `fibrosis` | 500 | 739 |
| Pleural Thickening | `pleural_thickening` | 499 | 631 |
| Nodule | `nodule` | 392 | 986 |
| Fracture | `fracture` | 389 | 661 |
| Atelectasis | `atelectasis` | 297 | 340 |
| Cardiomegaly | `cardiomegaly` | 293 | 293 |
| Calcification | `calcification` | 202 | 348 |
| Pneumothorax | `pneumothorax` | 189 | 211 |
| Emphysema | `emphysema` | 176 | 298 |
| Diffuse Nodule | `diffuse_nodule` | 169 | 290 |
| Mass | `mass` | 148 | 167 |

## Columns

| Column | Type | Notes |
|---|---|---|
| `image_id` | string | Upstream stem, e.g. `36199` — the pairing key |
| `image` | Image (**L**) | Radiograph, 1024×1024. Normalised to mode L — see below |
| `<class>_mask` ×13 | Image (L), **nullable** | `{0,255}`, 255 = abnormality. Union of that class's instances |
| `has_<class>_mask` ×13 | bool | Explicit presence flag |
| `<class>_fraction` ×13 | float32, nullable | Foreground area fraction of the frame |
| `annotation_json` | string | **Original upstream entry verbatim** — `syms`, `boxes`, per-instance `polygons` |
| `num_instances` | int32 | Total annotated instances on this image |
| `num_classes_present` | int32 | Distinct classes on this image (0–13) |
| `is_negative` | bool | True for the 611 images with no finding |
| `width`, `height` | int32 | Always 1024×1024 |
| `original_mode` | string | `L` or `RGBA` before normalisation |

## ⚠️ The 13 classes overlap at the pixel level

The ChestX-Det10 paper states it outright: *"multiple diseases might share the
same region of box."* Measured by rasterising all 9,639 polygons:

- **2,006 / 3,578** images carry more than one class
- **1,357 of those 2,006 (67.6 %)** have genuine **pixel** overlap between classes
- mean IoU **0.272** where overlap occurs
- Consolidation∩Effusion overlap in **770 of their 1,028** co-occurring images;
  Cardiomegaly∩Consolidation in **148 of 158**

They are therefore **13 independent binary masks, not one label map**. Collapsing
them into a single `{0..13}` image would silently erase whichever class is
composited first.

## ⚠️ An absent class is negative, not unlabelled

The radiologists annotated all 13 categories on every image, so a class with no
polygon is genuinely **absent** from that radiograph. Absence is stored as a
**null** mask plus a `has_<class>_mask` boolean — never infer it from an empty
mask. 86.4 % of the 13 × 3,578 cells are null.

Separately, **611 images (17.1 %) have no finding at all** (`is_negative`), and
**22.1 % of the annotated (image, class) pairs cover < 0.5 %** of the frame
(median 2.5 %). Any small-`n` debug sample will be badly unrepresentative.

## ⚠️ Image mode was normalised here

42 of the 3,578 upstream PNGs (37 train / 5 test) are mode **RGBA**; the other
3,536 are mode **L** — a quirk inherited from NIH ChestX-ray14. In all 42,
`R == G == B` exactly (max inter-channel difference 0) and alpha is uniformly
255, so they are grayscale wearing four channels and `.convert("L")` is
bit-for-bit lossless. **Every image here is mode L.** `original_mode` records
what it was upstream.

## ⚠️ No patient ID, and the NIH filename is destroyed

Deepwise renamed every file to an opaque integer (`36199` … `70958`). The NIH
ChestX-ray14 filename (`00000013_005.png`) is **not recoverable** and no mapping
is published. Consequences:

- **No patient grouping is possible.** NIH averages ~3.6 images per patient and
  the official split is described as random over images, so **intra-dataset
  patient leakage between train and test is likely and cannot be repaired.**
- **No cross-dataset de-duplication is possible.** Every image is a member of NIH
  ChestX-ray14 by construction. **SIIM-ACR Pneumothorax is the live hazard** —
  it is built on the same NIH source, so overlap with this dataset's Pneumothorax
  class is plausible and **unquantifiable**. Do not treat the two as independent
  benchmarks.
- No overlap with CheXpert, MIMIC-CXR, JSRT, Montgomery/Shenzhen, COVID-19
  Radiography or COVID-QU-Ex (all different source institutions).

## Ground truth — one adjudicated tier

Three board-certified radiologists in a 2+1 protocol. Two annotate every image
**independently and mutually blind** (the *committee*); a third with **15+ years'
experience** adjudicates, selecting among their annotations and adding any they
missed (the *judge*). Both papers designate the judge's output the **gold
standard** for train and test alike, while explicitly noting residual subjective
variation.

**Only the adjudicated result is released** — the two committee members'
individual annotations are not — so there is **no inter-rater agreement ceiling**
for this dataset and no rater or tier to choose between.

### Paper vs release: trust the release

The TMI paper's Table 1 disagrees slightly with the shipped JSON: **Nodule**
848/182 (paper) vs 816/170 (release) and **Diffuse Nodule** 195/51 vs 227/63 — a
clean transfer of exactly 32 train + 12 test instances between the two nodule
classes, i.e. a post-publication reclassification. Also Fracture 547→546 and
Effusion 379→378. Paper total 9,641 vs released **9,639**. The paper text says
"3,575 images" while 3,025 + 553 = **3,578** are released, matching the README.
The counts in this card are re-derived from the released JSONs.

## Provenance

Fetched from the official Deepwise host and GitHub repository — not a mirror.
Both zips match their `Content-Length` byte-exactly; every JSON filename maps 1:1
to a file on disk in both directions; all images are 1024×1024; instance totals
reconcile with the upstream README exactly. Three unofficial HF mirrors of this
dataset exist (`Lin-Mars/ChestX_Det`, `natealberti/ChestX-Det`) and none was
used. Note that the Kaggle mirror `mathurinache/chestxdetdataset` tags this
**CC BY-NC-SA 4.0**, which contradicts the upstream Apache-2.0 — an
uploader-assigned label, not the primary source.

## Attribution (required)

Per NIH terms, use of the underlying images requires all three of:

1. A link to the NIH download page: https://nihcc.app.box.com/v/ChestXray-NIHCC
2. Citation of Wang et al., CVPR 2017 (below)
3. Acknowledgement that the NIH Clinical Center is the data provider

## Citation

```bibtex
@article{lian2021sarnet,
  title   = {A Structure-Aware Relation Network for Thoracic Diseases Detection
             and Segmentation},
  author  = {Lian, Jie and Liu, Jingyu and Zhang, Shu and Gao, Kai and
             Liu, Xiaoqing and Zhang, Dingwen and Yu, Yizhou},
  journal = {IEEE Transactions on Medical Imaging},
  year    = {2021},
  eprint  = {2104.10326}
}

@inproceedings{liu2020chestxdet10,
  title     = {ChestX-Det10: Chest X-ray Dataset on Detection of Thoracic
               Abnormalities},
  author    = {Liu, Jingyu and Lian, Jie and Yu, Yizhou},
  booktitle = {arXiv:2006.10550},
  year      = {2020}
}

@inproceedings{wang2017chestxray8,
  title     = {ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on
               Weakly-Supervised Classification and Localization of Common Thorax
               Diseases},
  author    = {Wang, Xiaosong and Peng, Yifan and Lu, Le and Lu, Zhiyong and
               Bagheri, Mohammadhadi and Summers, Ronald M.},
  booktitle = {IEEE CVPR},
  pages     = {3462--3471},
  year      = {2017}
}
```