Healthcare
14 columns
200 rows
8 formats

Medical Imaging Diagnosis Labels

This dataset provides expertly labeled medical images with standardized diagnostic codes, modality and anatomical region metadata, and annotator confidence scores. Designed for supervised learning in medical AI, it enables robust model training, benchmarking, and workflow optimization for automated image diagnosis. The inclusion of label verification and detailed provenance supports clinical validation and regulatory compliance.

last updated 21 Jul 2025
by GoMask

The brief that made it

Training and benchmarking medical imaging AI models for disease detection

Sample rows

preview · 8 of 200 rows · all 14 columns

image_idstring	modalitystring	label_confidencefloat	label_verifiedboolean	body_partstring	patient_idstring	study_idstring	image_datedate	diagnosis_codestring	labeler_idstring	label_datedate	image_urlstring	diagnosis_labelstring	notesstring
IMG00001	X-ray	0.96	true	chest	PAT10234	STUDY-XR1023	2022-01-13	J18.9	RAD001	2022-01-14	https://deid.med/images/IMG00001.dcm	Pneumonia, unspecified organism	Consolidation in right lower lobe, clinical correlation advised.
IMG00002	CT	0.81	false	abdomen	PAT10235	STUDY-CT2041	2021-11-08	K35.80	RAD002	2021-11-09	https://deid.med/images/IMG00002.dcm	Acute appendicitis, unspecified	Inflamed appendix noted, no abscess formation.
IMG00003	X-ray	0.91	true	spine	PAT10236	STUDY-XR1024	2023-03-22	M54.5	RAD003	2023-03-22	https://deid.med/images/IMG00003.dcm	Low back pain	Mild disc space narrowing at L4-L5.
IMG00004	Ultrasound	0.88	false	pelvis	PAT10237	STUDY-US3091	2024-02-15	N83.201	RAD004	2024-02-17	https://deid.med/images/IMG00004.dcm	Unspecified ovarian cyst, right side	Simple cyst, follow-up recommended in 6 weeks.
IMG00005	X-ray	0.93	true	foot	PAT10238	STUDY-XR1025	2022-08-30	S92.001A	RAD005	2022-08-31	https://deid.med/images/IMG00005.dcm	Fracture of right calcaneus, initial	Displaced fracture, orthopedic referral advised.
IMG00006	CT	0.67	false	lung	PAT10239	STUDY-CT2042	2020-05-21	J84.10	RAD006	2020-05-22	https://deid.med/images/IMG00006.dcm	Pulmonary fibrosis, unspecified	Diffuse reticulations and honeycombing seen bilaterally.
IMG00007	MRI	0.81	false	brain	PAT10240	STUDY-MR5014	2023-10-12	G93.40	RAD007	2023-10-14	https://deid.med/images/IMG00007.dcm	Encephalopathy, unspecified	Mild T2 hyperintensity in parietal white matter.
IMG00008	Ultrasound	0.87	true	thyroid	PAT10241	STUDY-US3092	2021-06-18	E04.1	RAD008	2021-06-19	https://deid.med/images/IMG00008.dcm	Nontoxic single thyroid nodule	Single hypoechoic nodule, benign features noted.
IMG00009	X-ray	0.98	true	chest	PAT10242	STUDY-XR1026	2024-04-02	R91.8	RAD009	2024-04-03	https://deid.med/images/IMG00009.dcm	Other nonspecific abnormal finding of lung field	No acute infiltrate, mild increased markings.
IMG00010	PET	0.74	false	whole body	PAT10243	STUDY-PE7011	2020-12-10	C80.1	RAD010	2020-12-12	https://deid.med/images/IMG00010.dcm	Malignant neoplasm, unspecified site	Multiple regions of increased uptake, further workup needed.
IMG00011	X-ray	0.89	false	spine	PAT10244	STUDY-XR1027	2021-09-17	M51.26	RAD011	2021-09-18	https://deid.med/images/IMG00011.dcm	Other intervertebral disc displacement, lumbar region	Disc bulge at L3-L4 with mild narrowing.
IMG00012	Mammography	0.83	true	breast	PAT10245	STUDY-MG4021	2022-03-23	D48.6	RAD012	2022-03-25	https://deid.med/images/IMG00012.dcm	Neoplasm of uncertain behavior of breast	Suspicious mass in upper outer quadrant, BI-RADS 4.
IMG00013	CT	0.7	false	lung	PAT10246	STUDY-CT2043	2022-06-11	J44.9	RAD013	2022-06-11	https://deid.med/images/IMG00013.dcm	Chronic obstructive pulmonary disease, unspecified	Emphysematous changes, no acute infiltrate.
IMG00014	Ultrasound	0.82	true	abdomen	PAT10247	STUDY-US3093	2023-12-09	K80.20	RAD014	2023-12-10	https://deid.med/images/IMG00014.dcm	Calculus of gallbladder without cholecystitis	Multiple gallstones, no wall thickening.
IMG00015	Mammography	0.59	false	breast	PAT10248	STUDY-MG4022	2021-10-29	C50.919	RAD015	2021-10-30	https://deid.med/images/IMG00015.dcm	Malignant neoplasm of unspecified site of right female breast	Irregular spiculated mass, requires biopsy.
IMG00016	X-ray	0.79	true	hand	PAT10249	STUDY-XR1028	2024-05-01	S62.501A	RAD016	2024-05-02	https://deid.med/images/IMG00016.dcm	Fracture of right metacarpal bone, initial	Transverse fracture at fifth metacarpal head.
IMG00017	PET	0.81	false	brain	PAT10250	STUDY-PE7012	2023-08-13	C71.9	RAD017	2023-08-14	https://deid.med/images/IMG00017.dcm	Malignant neoplasm of brain, unspecified	Focal area of increased FDG uptake in frontal lobe.
IMG00018	Ultrasound	0.91	true	fetal heart	PAT10251	STUDY-US3094	2022-09-20	Q24.9	RAD018	2022-09-21	https://deid.med/images/IMG00018.dcm	Congenital malformation of heart, unspecified	Four-chamber view normal, no septal defect identified.
IMG00019	CT	0.75	false	abdomen	PAT10252	STUDY-CT2044	2023-07-14	K57.30	RAD019	2023-07-15	https://deid.med/images/IMG00019.dcm	Diverticulosis of large intestine without perforation or abscess	Multiple colonic diverticula, no evidence of inflammation.
IMG00020	X-ray	0.92	true	lung	PAT10253	STUDY-XR1029	2022-07-07	J43.9	RAD020	2022-07-08	https://deid.med/images/IMG00020.dcm	Emphysema, unspecified	Hyperlucency and flattened diaphragms bilaterally.

What the 200 rows show

from the 200-row sample

39%label_verified = true
0.82median label_confidence

modality200 rows by value

label_confidence196 rows, in bands of 0.1

Median 0.82, from 0.0 to 1.0.

body_part200 rows · top 10 of 13 values

abdomen33
chest28
lung19
spine18
foot15
breast15
brain13
hand13
pelvis12
whole body10

14 columns by typefrom the column list below

string 10
float 1
date 2
boolean 1

Columns

14 columns in four groups

blueprint · 14 columns
column	type	description	example
Text 10 columns
`image_id`	string	Unique identifier for each medical image in the dataset.unique	IMG00001
`patient_id`	string	Anonymized unique identifier for the patient associated with the image.	PAT10234
`study_id`	string	Identifier for the imaging study or exam session.	STUDY-XR1023
`modality`	string	Type of imaging modality used (e.g., X-ray, MRI, CT, Ultrasound).7 values	X-ray
`body_part`	string	Body part or anatomical region imaged (e.g., chest, brain, abdomen).	chest
`image_url`	string	URL or file path to access the medical image (de-identified).	https://deid.med/images/I…
`diagnosis_code`	string	Standardized diagnosis code (e.g., ICD-10, SNOMED) assigned to the image.	J18.9
`diagnosis_label`	string	Human-readable diagnosis label corresponding to the diagnosis code.	Low back pain
`labeler_id`	string	Identifier for the radiologist or annotator who provided the diagnosis label.	RAD001
`notes`	string	Additional comments or notes provided by the labeler regarding the diagnosis.optional	Consolidation in right lo…
Numbers 1 column
`label_confidence`	float	Confidence score (0.0–1.0) indicating the certainty of the diagnosis label.0 to 1 · optional	0.96
Dates and times 2 columns
`image_date`	date	Date when the image was acquired.	2022-01-13
`label_date`	date	Date when the diagnosis label was assigned.	2022-01-14
True or false 1 column
`label_verified`	boolean	Indicates if the diagnosis label was verified by a second expert.optional	true

Use it for

A healthcare dashboard
The label_verified rate, label_confidence by modality and a breakdown of body_part. Excel, Power BI or Tableau.
Why do 78 of 200 rows have label_verified = true?
A class exercise
Hand out the rows and one question. Everyone works from the same 200 rows.
Images200
IMG000010.96X-ray
IMG000020.81CT
IMG000030.91X-ray
A software demo
Believable images with patient_id, study_id and modality to fill a screen in front of a buyer.

This dataset200 rows14 columns

Yours1,000 rows14 columns

blueprint · medical-imaging-diagnosis-labels

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 14 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 linked to diagnosis label.
Image modality and quality annotated.
Patient age group included.
Ambiguous cases flagged.

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: medical-imaging-diagnosis-labels

What should your data show?

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Sample rows

What the 200 rows show

Columns

Use it for

A healthcare dashboard

A class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Healthcare

What should your data show?