Healthcare
20 columns
200 rows
8 formats

Labeled Healthcare Image Annotations

This dataset contains synthetic, labeled medical images (such as X-rays and MRIs) with detailed annotations for use in computer vision model training and diagnostic tool development. Each record includes image metadata, patient demographics, annotation details (including spatial data and confidence scores), and diagnostic labels, making it ideal for research, benchmarking, and algorithm validation in medical imaging.

last updated 12 Jul 2025
by GoMask

The brief that made it

Training and benchmarking computer vision models for medical image analysis

Sample rows

preview · 8 of 200 rows · all 20 columns

image_idstring	image_modalitystring	annotation_confidencefloat	annotation_labelstring	file_namestring	body_partstring	image_widthinteger	image_heightinteger	image_resolution_dpiinteger	patient_idstring	patient_ageinteger	patient_sexstring	acquisition_datedate	annotation_idstring	annotator_idstring	annotation_datedatetime	annotation_typestring	annotation_coordinatesstring	diagnosisstring	notesstring
IMG001	X-ray	0.92	pneumonia	chest_Xray_PAT101_2022-11-03.png	chest	2048	2048	150	PAT101	57	female	2022-11-03	ANN001	DOC001	2022-11-04T10:13:24Z	bounding_box	x:123,y:456,w:321,h:654	pneumonia	blank
IMG001	X-ray	0.87	pneumonia	chest_Xray_PAT101_2022-11-03.png	chest	2048	2048	150	PAT101	57	female	2022-11-03	ANN002	ALG001	2022-11-05T08:45:10Z	classification	blank	pneumonia	AI agrees with human label
IMG002	CT	0.95	tumor	abdomen_CT_PAT217_2021-09-15.dcm	abdomen	1024	1024	300	PAT217	62	male	2021-09-15	ANN003	DOC002	2021-09-16T14:21:51Z	segmentation	mask:0,0,1,1,2,2,3,3	tumor	blank
IMG002	CT	0.82	tumor	abdomen_CT_PAT217_2021-09-15.dcm	abdomen	1024	1024	300	PAT217	62	male	2021-09-15	ANN004	ALG002	2021-09-17T09:11:32Z	classification	blank	tumor	blank
IMG003	X-ray	0.98	normal	lung_Xray_PAT109_2023-01-21.jpg	lung	4096	3072	96	PAT109	44	male	2023-01-21	ANN005	DOC003	2023-01-22T15:02:10Z	classification	blank	normal	blank
IMG004	X-ray	0.81	greenstick fracture	hand_Xray_PAT203_2021-10-11.bmp	hand	2048	2048	150	PAT203	12	male	2021-10-11	ANN006	DOC004	2021-10-12T08:40:22Z	keypoints	x:230,y:345;x:250,y:355	greenstick fracture	pediatric case
IMG005	MRI	0.91	soft_tissue_mass	pelvis_MRI_PAT301_2020-06-18.dcm	pelvis	512	512	240	PAT301	34	male	2020-06-18	ANN007	DOC005	2020-06-18T18:35:05Z	segmentation	mask:2,7,15,23,29,44,50,76	soft_tissue_mass	blank
IMG006	MRI	0.97	tumor	brain_MRI_PAT305_2023-04-03.dcm	brain	1024	768	300	PAT305	29	female	2023-04-03	ANN008	DOC006	2023-04-05T09:11:50Z	segmentation	mask:10,50,80,120,210,300	tumor	blank
IMG007	Ultrasound	0.88	cyst	knee_Ultrasound_PAT206_2021-02-14.png	knee	960	800	150	PAT206	41	female	2021-02-14	ANN009	DOC007	2021-02-15T07:22:31Z	segmentation	mask:100,150,200,300,350,400	benign cyst	blank
IMG008	CT	0.68	calcification	liver_CT_PAT209_2022-08-30.dcm	liver	2048	2048	300	PAT209	67	female	2022-08-30	ANN010	ALG003	2022-08-31T12:35:40Z	classification	blank	calcification	blank
IMG009	Ultrasound	0.74	splenic_mass	spleen_Ultrasound_PAT199_2021-07-19.jpg	spleen	1200	960	200	PAT199	35	male	2021-07-19	ANN011	DOC008	2021-07-20T09:33:55Z	segmentation	mask:80,110,130,190,220,260	splenic_mass, benign	blank
IMG010	Other	0.29	diabetic_retinopathy	retina_Other_PAT999_2020-05-01.tif	retina	600	900	350	blank	blank	unknown	2020-05-01	ANN012	ALG004	2020-05-01T16:10:09Z	classification	blank	diabetic_retinopathy (unsure)	minimal info
IMG011	Other	0.33	degeneration	spine_Other_PAT998_2021-12-10.img	spine	1200	1800	72	PAT998	38	other	2021-12-10	ANN013	ALG005	2021-12-12T10:55:10Z	bounding_box	x:15,y:26,w:80,h:90	degeneration (rare)	rare modality
IMG012	Ultrasound	0.84	thyroid_nodule	neck_Ultrasound_PAT210_2023-01-06.jpg	neck	640	800	150	PAT210	53	female	2023-01-06	ANN014	DOC009	2023-01-07T10:48:22Z	segmentation	mask:20,40,120,150,220,300	benign thyroid_nodule	blank
IMG013	PET	0.78	ischemia	brain_PET_PAT401_2023-02-15.dcm	brain	512	512	72	PAT401	41	male	2023-02-15	ANN015	DOC010	2023-02-16T13:18:41Z	segmentation	mask:25,40,90,170,255,310	ischemia	blank
IMG014	X-ray	0.93	fracture	foot_Xray_PAT207_2022-03-10.png	foot	1024	1024	150	PAT207	26	male	2022-03-10	ANN016	DOC011	2022-03-11T08:53:20Z	bounding_box	x:80,y:110,w:60,h:120	fracture	mild displacement
IMG015	CT	0.77	hemorrhage	brain_CT_PAT402_2020-10-12.dcm	brain	512	512	400	PAT402	2	female	2020-10-12	ANN017	DOC012	2020-10-13T10:05:30Z	segmentation	mask:7,16,23,30,44,51	hemorrhage (neonatal)	neonate
IMG016	CT	0.66	calcification	liver_CT_PAT403_2022-11-22.dcm	liver	2560	2048	300	PAT403	76	female	2022-11-22	ANN018	ALG006	2022-11-24T17:13:28Z	classification	blank	calcification	blank
IMG017	Ultrasound	0.82	cyst	kidney_Ultrasound_PAT212_2022-07-09.jpg	kidney	800	600	120	PAT212	46	female	2022-07-09	ANN019	DOC013	2022-07-10T11:43:29Z	segmentation	mask:30,50,90,105,140,170	benign cyst	blank
IMG018	X-ray	0.88	greenstick fracture	hand_Xray_PAT214_2023-03-25.bmp	hand	1024	1024	96	PAT214	9	female	2023-03-25	ANN020	DOC014	2023-03-26T15:27:11Z	bounding_box	x:110,y:139,w:30,h:51	greenstick fracture	child

What the 200 rows show

from the 200-row sample

Other (image modality) stands out: mean annotation_confidence is 0.25, against 0.88 for the rest.

0.88median annotation_confidence
4patient sexes
4annotation types
15body parts
43diagnosises
1,024median image_width

Mean annotation_confidence by image_modality200 rows

annotation_confidence200 rows, in bands of 0.1

Median 0.88, from 0.13 to 0.99.

annotation_label200 rows · top 10 of 18 values

tumor35
pneumonia17
normal17
fracture15
soft_tissue_mass13
splenic_mass13
calcification12
diabetic_retinopathy12
ischemia12
degeneration11

20 columns by typefrom the column list below

string 13
integer 4
float 1
date 1
datetime 1

Columns

20 columns in three groups

blueprint · 20 columns
column	type	description	example
Text 13 columns
`image_id`	string	Unique identifier for each medical image in the dataset.unique	IMG001
`file_name`	string	Original file name of the medical image.	lung_Xray_2023PAT162.png
`image_modality`	string	Type of medical imaging modality used (e.g., X-ray, MRI, CT, Ultrasound).6 values	X-ray
`body_part`	string	Anatomical region captured in the image (e.g., chest, brain, abdomen).	chest
`patient_id`	string	Synthetic identifier for the patient associated with the image.optional	PAT101
`patient_sex`	string	Sex of the patient.male · female · other · unknown · optional	female
`annotation_id`	string	Unique identifier for the annotation record.unique	ANN001
`annotator_id`	string	Identifier for the annotator (human or algorithm) who labeled the image.	DOC001
`annotation_type`	string	Type of annotation (e.g., bounding_box, segmentation, classification, keypoints).5 values	bounding_box
`annotation_label`	string	Label or class assigned to the annotated region (e.g., pneumonia, tumor, normal).	pneumonia
`annotation_coordinates`	string	Coordinates or mask data for the annotation (e.g., bounding box coordinates, segmentation mask points).optional	x:123,y:456,w:321,h:654
`diagnosis`	string	Diagnosis or finding associated with the image or annotation.optional	pneumonia
`notes`	string	Additional notes or comments about the image or annotation.optional	pediatric case
Numbers 5 columns
`image_width`	integer	Width of the image in pixels.1 or more	2048
`image_height`	integer	Height of the image in pixels.1 or more	2048
`image_resolution_dpi`	integer	Image resolution in dots per inch (DPI).1 or more · optional	150
`patient_age`	integer	Age of the patient in years.0 to 120 · optional	57
`annotation_confidence`	float	Confidence score (0-1) indicating certainty of the annotation.0 to 1 · optional	0.92
Dates and times 2 columns
`acquisition_date`	date	Date when the image was acquired.optional	2022-11-03
`annotation_date`	datetime	Date and time when the annotation was made.	2022-11-04T10:13:24Z

Use it for

A healthcare dashboard
Annotation_confidence by image_modality and a breakdown of annotation_label. Excel, Power BI or Tableau.
Why do the 24 Other rows have a mean annotation_confidence of 0.25?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Images200
IMG0010.92X-ray
IMG0010.87X-ray
IMG0020.95CT
A software demo
Believable images with file_name, image_modality and body_part to fill a screen in front of a buyer.

This dataset200 rows20 columns

Yours1,000 rows20 columns

blueprint · labeled-healthcare-image-annotations

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 20 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Each image paired with structured metadata
Classes include common and rare pathologies
Annotations by region of interest
No real patient data included

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: labeled-healthcare-image-annotations

What should your data show?

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Sample rows

What the 200 rows show

Columns

Use it for

A healthcare dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Healthcare

What should your data show?