metadata
dataset_info:
- config_name: Derm7pt_clinical
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': basal_cell_carcinoma
'1': blue_nevus
'2': clark_nevus
'3': combined_nevus
'4': congenital_nevus
'5': dermal_nevus
'6': dermatofibroma
'7': lentigo
'8': melanoma_(0.76_to_1.5_mm)
'9': melanoma_(in_situ)
'10': melanoma_(less_than_0.76_mm)
'11': melanoma_(more_than_1.5_mm)
'12': melanoma_metastasis
'13': melanosis
'14': miscellaneous
'15': recurrent_nevus
'16': reed_or_spitz_nevus
'17': seborrheic_keratosis
'18': vascular_lesion
splits:
- name: train
num_bytes: 77034176
num_examples: 983
download_size: 77079805
dataset_size: 77034176
- config_name: Derm7pt_derm
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': basal_cell_carcinoma
'1': blue_nevus
'2': clark_nevus
'3': combined_nevus
'4': congenital_nevus
'5': dermal_nevus
'6': dermatofibroma
'7': lentigo
'8': melanoma_(0.76_to_1.5_mm)
'9': melanoma_(in_situ)
'10': melanoma_(less_than_0.76_mm)
'11': melanoma_(more_than_1.5_mm)
'12': melanoma_metastasis
'13': melanosis
'14': miscellaneous
'15': recurrent_nevus
'16': reed_or_spitz_nevus
'17': seborrheic_keratosis
'18': vascular_lesion
splits:
- name: train
num_bytes: 71784912
num_examples: 983
download_size: 71830364
dataset_size: 71784912
- config_name: ISBI2016
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': benign
'1': malignant
splits:
- name: train
num_bytes: 78215748.888
num_examples: 1272
download_size: 78789964
dataset_size: 78215748.888
- config_name: ISBI2017_reduced
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': melanoma
'1': nevus
'2': seborrheic_keratosis
splits:
- name: train
num_bytes: 15038674.5
num_examples: 2750
download_size: 14635040
dataset_size: 15038674.5
- config_name: ISBI2018_reduced
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': AKIEC
'1': BCC
'2': BKL
'3': DF
'4': MEL
'5': NV
'6': VASC
splits:
- name: train
num_bytes: 68984776.24
num_examples: 11720
download_size: 61323728
dataset_size: 68984776.24
- config_name: ISBI2019
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': AK
'1': BCC
'2': BKL
'3': DF
'4': MEL
'5': NV
'6': SCC
'7': VASC
splits:
- name: train
num_bytes: 1435928165.8
num_examples: 25320
download_size: 1523395483
dataset_size: 1435928165.8
- config_name: ISBI2020
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': benign
'1': malignant
splits:
- name: train
num_bytes: 2015944554.012
num_examples: 33124
download_size: 2015516827
dataset_size: 2015944554.012
- config_name: MED-NODE
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': benign
'1': malignant
splits:
- name: train
num_bytes: 11730503
num_examples: 170
download_size: 11739181
dataset_size: 11730503
- config_name: MSK-1
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': benign
'1': malignant
splits:
- name: train
num_bytes: 78457089.795
num_examples: 1085
download_size: 78491569
dataset_size: 78457089.795
- config_name: MSK-2
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': benign
'1': malignant
splits:
- name: train
num_bytes: 93393438.401
num_examples: 1519
download_size: 94302344
dataset_size: 93393438.401
- config_name: MSK-3
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': benign
'1': malignant
splits:
- name: train
num_bytes: 13516428
num_examples: 221
download_size: 13527718
dataset_size: 13516428
- config_name: MSK-4
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': benign
'1': malignant
splits:
- name: train
num_bytes: 55323454
num_examples: 939
download_size: 55365404
dataset_size: 55323454
- config_name: PH2
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': benign
'1': malignant
splits:
- name: train
num_bytes: 14560527
num_examples: 200
download_size: 14570212
dataset_size: 14560527
- config_name: SDC-198
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Acne_Keloidalis_Nuchae
'1': Acne_Vulgaris
'2': Acrokeratosis_Verruciformis
'3': Actinic_solar_Damage(Actinic_Cheilitis)
'4': Actinic_solar_Damage(Actinic_Keratosis)
'5': Actinic_solar_Damage(Cutis_Rhomboidalis_Nuchae)
'6': Actinic_solar_Damage(Pigmentation)
'7': Actinic_solar_Damage(Solar_Elastosis)
'8': Actinic_solar_Damage(Solar_Purpura)
'9': Actinic_solar_Damage(Telangiectasia)
'10': Acute_Eczema
'11': Allergic_Contact_Dermatitis
'12': Alopecia_Areata
'13': Androgenetic_Alopecia
'14': Angioma
'15': Angular_Cheilitis
'16': Aphthous_Ulcer
'17': Apocrine_Hydrocystoma
'18': Arsenical_Keratosis
'19': Balanitis_Xerotica_Obliterans
'20': Basal_Cell_Carcinoma
'21': Beau's_Lines
'22': Becker's_Nevus
'23': Behcet's_Syndrome
'24': Benign_Keratosis
'25': Blue_Nevus
'26': Bowen's_Disease
'27': Bowenoid_Papulosis
'28': Cafe_Au_Lait_Macule
'29': Callus
'30': Candidiasis
'31': Cellulitis
'32': Chalazion
'33': Clubbing_of_Fingers
'34': Compound_Nevus
'35': Congenital_Nevus
'36': Crowe's_Sign
'37': Cutanea_Larva_Migrans
'38': Cutaneous_Horn
'39': Cutaneous_T-Cell_Lymphoma
'40': Cutis_Marmorata
'41': Darier-White_Disease
'42': Dermatofibroma
'43': Dermatosis_Papulosa_Nigra
'44': Desquamation
'45': Digital_Fibroma
'46': Dilated_Pore_of_Winer
'47': Discoid_Lupus_Erythematosus
'48': Disseminated_Actinic_Porokeratosis
'49': Drug_Eruption
'50': Dry_Skin_Eczema
'51': Dyshidrosiform_Eczema
'52': Dysplastic_Nevus
'53': Eccrine_Poroma
'54': Eczema
'55': Epidermal_Nevus
'56': Epidermoid_Cyst
'57': Epithelioma_Adenoides_Cysticum
'58': Erythema_Ab_Igne
'59': Erythema_Annulare_Centrifigum
'60': Erythema_Craquele
'61': Erythema_Multiforme
'62': Exfoliative_Erythroderma
'63': Factitial_Dermatitis
'64': Favre_Racouchot
'65': Fibroma
'66': Fibroma_Molle
'67': Fixed_Drug_Eruption
'68': Follicular_Mucinosis
'69': Follicular_Retention_Cyst
'70': Fordyce_Spots
'71': Frictional_Lichenoid_Dermatitis
'72': Ganglion
'73': Geographic_Tongue
'74': Granulation_Tissue
'75': Granuloma_Annulare
'76': Green_Nail
'77': Guttate_Psoriasis
'78': Hailey_Hailey_Disease
'79': Half_and_Half_Nail
'80': Halo_Nevus
'81': Herpes_Simplex_Virus
'82': Herpes_Zoster
'83': Hidradenitis_Suppurativa
'84': Histiocytosis_X
'85': Hyperkeratosis_Palmaris_Et_Plantaris
'86': Hypertrichosis
'87': Ichthyosis
'88': Impetigo
'89': Infantile_Atopic_Dermatitis
'90': Inverse_Psoriasis
'91': Junction_Nevus
'92': Keloid
'93': Keratoacanthoma
'94': Keratolysis_Exfoliativa_of_Wende
'95': Keratosis_Pilaris
'96': Kerion
'97': Koilonychia
'98': Kyrle's_Disease
'99': Leiomyoma
'100': Lentigo_Maligna_Melanoma
'101': Leukocytoclastic_Vasculitis
'102': Leukonychia
'103': Lichen_Planus
'104': Lichen_Sclerosis_Et_Atrophicus
'105': Lichen_Simplex_Chronicus
'106': Lichen_Spinulosis
'107': Linear_Epidermal_Nevus
'108': Lipoma
'109': Livedo_Reticularis
'110': Lymphangioma_Circumscriptum
'111': Lymphocytic_Infiltrate_of_Jessner
'112': Lymphomatoid_Papulosis
'113': Mal_Perforans
'114': Malignant_Melanoma
'115': Median_Nail_Dystrophy
'116': Melasma
'117': Metastatic_Carcinoma
'118': Milia
'119': Molluscum_Contagiosum
'120': Morphea
'121': Mucha_Habermann_Disease
'122': Mucous_Membrane_Psoriasis
'123': Myxoid_Cyst
'124': Nail_Dystrophy
'125': Nail_Nevus
'126': Nail_Psoriasis
'127': Nail_Ridging
'128': Neurodermatitis
'129': Neurofibroma
'130': Neurotic_Excoriations
'131': Nevus_Comedonicus
'132': Nevus_Incipiens
'133': Nevus_Sebaceous_of_Jadassohn
'134': Nevus_Spilus
'135': Nummular_Eczema
'136': Onychogryphosis
'137': Onycholysis
'138': Onychomycosis
'139': Onychoschizia
'140': Paronychia
'141': Pearl_Penile_Papules
'142': Perioral_Dermatitis
'143': Pincer_Nail_Syndrome
'144': Pitted_Keratolysis
'145': Pityriasis_Alba
'146': Pityriasis_Rosea
'147': Pityrosporum_Folliculitis
'148': Poikiloderma_Atrophicans_Vasculare
'149': Pomade_Acne
'150': Pseudofolliculitis_Barbae
'151': Pseudorhinophyma
'152': Psoriasis
'153': Pustular_Psoriasis
'154': Pyoderma_Gangrenosum
'155': Pyogenic_Granuloma
'156': Racquet_Nail
'157': Radiodermatitis
'158': Rhinophyma
'159': Rosacea
'160': Scalp_Psoriasis
'161': Scar
'162': Scarring_Alopecia
'163': Schamberg's_Disease
'164': Sebaceous_Gland_Hyperplasia
'165': Seborrheic_Dermatitis
'166': Seborrheic_Keratosis
'167': Skin_Tag
'168': Solar_Lentigo
'169': Stasis_Dermatitis
'170': Stasis_Edema
'171': Stasis_Ulcer
'172': Steroid_Acne
'173': Steroid_Striae
'174': Steroid_Use_abusemisuse_Dermatitis
'175': Stomatitis
'176': Strawberry_Hemangioma
'177': Striae
'178': Subungual_Hematoma
'179': Syringoma
'180': Terry's_Nails
'181': Tinea_Corporis
'182': Tinea_Cruris
'183': Tinea_Faciale
'184': Tinea_Manus
'185': Tinea_Pedis
'186': Tinea_Versicolor
'187': Toe_Deformity
'188': Trichilemmal_Cyst
'189': Trichofolliculoma
'190': Trichostasis_Spinulosa
'191': Ulcer
'192': Urticaria
'193': Varicella
'194': Verruca_Vulgaris
'195': Vitiligo
'196': Wound_Infection
'197': Xerosis
splits:
- name: train
num_bytes: 491299125.831
num_examples: 6401
download_size: 474684388
dataset_size: 491299125.831
- config_name: UDA-1
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': benign
'1': malignant
splits:
- name: train
num_bytes: 35607838
num_examples: 553
download_size: 35563133
dataset_size: 35607838
- config_name: UDA-2
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': benign
'1': malignant
splits:
- name: train
num_bytes: 3467866
num_examples: 57
download_size: 3471677
dataset_size: 3467866
configs:
- config_name: Derm7pt_clinical
data_files:
- split: train
path: Derm7pt_clinical/train-*
- config_name: Derm7pt_derm
data_files:
- split: train
path: Derm7pt_derm/train-*
- config_name: ISBI2016
data_files:
- split: train
path: ISBI2016/train-*
- config_name: ISBI2017_reduced
data_files:
- split: train
path: ISBI2017_reduced/train-*
- config_name: ISBI2018_reduced
data_files:
- split: train
path: ISBI2018_reduced/train-*
- config_name: ISBI2019
data_files:
- split: train
path: ISBI2019/train-*
- config_name: ISBI2020
data_files:
- split: train
path: ISBI2020/train-*
- config_name: MED-NODE
data_files:
- split: train
path: MED-NODE/train-*
- config_name: MSK-1
data_files:
- split: train
path: MSK-1/train-*
- config_name: MSK-2
data_files:
- split: train
path: MSK-2/train-*
- config_name: MSK-3
data_files:
- split: train
path: MSK-3/train-*
- config_name: MSK-4
data_files:
- split: train
path: MSK-4/train-*
- config_name: PH2
data_files:
- split: train
path: PH2/train-*
- config_name: SDC-198
data_files:
- split: train
path: SDC-198/train-*
- config_name: UDA-1
data_files:
- split: train
path: UDA-1/train-*
- config_name: UDA-2
data_files:
- split: train
path: UDA-2/train-*
Dataset Card for dermatological diagnoses
This dataset is aimed at grouping and facilitating access to large volumes of data on the diagnosis of dermatological lesions. The datasets come from various sources and have been preprocessed and standardized so that they can be immediately used with your preferred model. The resolution of these is 224x224.
You can find 16 datasets that have been used in several articles and competitions on the subject, from 2 to 198 categories. The DERM-LIB dataset is not included as it requires a license that can be obtained here: https://licensing.edinburgh-innovations.ed.ac.uk/product/dermofit-image-library.
Below, we present examples of how you can download and use each of them.
How to use
from datasets import load_dataset
data_names = [
"Derm7pt_clinical",
"Derm7pt_derm",
"MED-NODE",
"MSK-1",
"MSK-2",
"MSK-3",
"MSK-4",
"PH2",
"SDC-198",
"UDA-1",
"UDA-2",
"ISBI2016",
"ISBI2017_reduced",
"ISBI2018_reduced",
"ISBI2019",
"ISBI2020",
]
for dataset in data_names:
loaded = load_dataset(
"z72pepee/dermatological-diagnoses",
dataset,
token="YOUR_TOKEN",
)
print("Dataset", dataset)
print("Categories", loaded["train"].features["label"])
print("Instances", loaded["train"].num_rows)
print("Example 1", loaded["train"][0])
- Funded by: Research group KDIS (Knowledge Discovery and Intelligent Systems), University of Cordoba, Spain, https://www.uco.es/kdis/.