DOI: 10.7910/dvn/fsxraq
title: MILK10k: A hierarchical multimodal imaging learning toolkit for diagnosing pigmented and non-pigmented skin cancer and its simulators

description: &lt;p&gt;&lt;strong&gt;MILK10k&lt;/strong&gt; consists of 10480 images, each representing a paired clinical close-up and dermatoscopic image for 5240 lesions. The dataset’s metadata include:&lt;/p&gt;
&lt;ul&gt;
 &lt;li&gt;Lesion ID&lt;/li&gt;
 &lt;li&gt;Image type&lt;/li&gt;
 &lt;li&gt;ISIC archive ID / Image name&lt;/li&gt;
 &lt;li&gt;Information on image manipulations&lt;/li&gt;
 &lt;li&gt;Age (in 5-year intervals)&lt;/li&gt;
 &lt;li&gt;Sex&lt;/li&gt;
 &lt;li&gt;Skin tone&lt;/li&gt;
 &lt;li&gt;Anatomic site&lt;/li&gt;
 &lt;li&gt;Descriptive MONET-image features&lt;/li&gt;
 &lt;li&gt;Diagnosis&lt;/li&gt;
 &lt;li&gt;Method of ground truth confirmation&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Skin tone is categorized into six levels, ranging from very dark (0) to very light (5), intentionally distinct from the Fitzpatrick skin types to avoid confusion. Most patients had skin tones in the middle ranges. Of the 5240 lesions, 95.7% were biopsied or excised, with histopathology serving as the gold standard for diagnosis. Diagnoses were mapped to both the ISIC-Dx diagnostic scheme and a simplified classification based on the ISIC2018/2019 challenge and HAM10000 diagnostic categories. The dataset includes 11 simplified diagnostic categories:&lt;/p&gt;
&lt;ul&gt;
 &lt;li&gt;Basal cell carcinoma (BCC)&lt;/li&gt;
 &lt;li&gt;Melanocytic nevus (NV)&lt;/li&gt;
 &lt;li&gt;Benign keratinocytic lesion (BKL)&lt;/li&gt;
 &lt;li&gt;Squamous cell carcinoma/keratoacanthoma (SCCKA)&lt;/li&gt;
 &lt;li&gt;Melanoma (MEL)&lt;/li&gt;
 &lt;li&gt;Actinic keratosis/intraepidermal carcinoma (AKIEC)&lt;/li&gt;
 &lt;li&gt;Dermatofibroma (DF)&lt;/li&gt;
 &lt;li&gt;Inflammatory and infectious conditions (INF)&lt;/li&gt;
 &lt;li&gt;Vascular lesions and hemorrhage (VASC)&lt;/li&gt;
 &lt;li&gt;Other benign proliferations including collision tumors (BEN_OTH)&lt;/li&gt;
 &lt;li&gt;Other malignant proliferations including collision tumors (MAL_OTH)&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;Additionally, we provide the most specific ISIC-Dx diagnosis and its parent branch in the ISIC-Dx diagnostic tree. In cases where a dermatoscopic image of the same lesion was already included in the ISIC archive, its corresponding existing ISIC lesion identifier is reported in the metadata. All images have been annotated using the MONET framework, with probabilities for the following concept term groups included in the metadata:&lt;/p&gt;
&lt;ul&gt;
 &lt;li&gt;Ulceration, crust&lt;/li&gt;
 &lt;li&gt;Hair&lt;/li&gt;
 &lt;li&gt;Vasculature, vessels&lt;/li&gt;
 &lt;li&gt;Erythema&lt;/li&gt;
 &lt;li&gt;Pigmentation&lt;/li&gt;
 &lt;li&gt;Gel, water drop, fluid, dermoscopy liquid&lt;/li&gt;
 &lt;li&gt;Skin markings, pen ink, purple pen&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;In addition to MILK10k, we have curated a smaller benchmark dataset, called &lt;strong&gt;MILK10k Benchmark&lt;/strong&gt; derived from the same sources and covering the same diagnostic categories. This dataset is available as part of a live challenge within the ISIC framework and can be accessed on ISIC.&lt;/p&gt;
&lt;p&gt;Images were provided by the following institutions:&lt;/p&gt;
&lt;ul&gt;
 &lt;li&gt;Department of Dermatology, Medical University of Vienna, Vienna, Austria&lt;/li&gt;
 &lt;li&gt;Medicine Faculty Department of Dermatology, Ankara University, Ankara, Turkey&lt;/li&gt;
 &lt;li&gt;Mayne Academy of General Practice, Medical School, The University of Queensland, Australia&lt;/li&gt;
 &lt;li&gt;Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, USA&lt;/li&gt;
 &lt;li&gt;Independent Researcher, 1000 Skopje, North Macedonia&lt;/li&gt;
&lt;/ul&gt;