The visuAAL Skin Segmentation Dataset contains 46,775 high quality images divided into a training set with 45,623 images, and a validation set with 1,152 images. Skin areas have been obtained automatically from the FashionPedia garment dataset. The process to extract the skin areas is explained in detail in the paper 'From Garment to Skin: The visuAAL Skin Segmentation Dataset'.

If you use the visuAAL Skin Segmentation Dataset, please, cite:

• https://doi.org/10.5281/zenodo.6973396
• https://doi.org/10.1007/978-3-031-13321-3_6

How to use:

1. Download the FashionPedia dataset from https://fashionpedia.github.io/home/Fashionpedia_download.html
2. Download the visuAAL Skin Segmentation Dataset. The dataset consists of two folders, namely train_masks and val_masks. Each folder corresponds to the training and validation sets in the original FashionPedia dataset.
3. After extracting the images from FashionPedia, for each image existing in the visuAAL skin segmentation dataset, the original image can be found with the same name (file_name in the annotations file).

A sample of image data in the FashionPedia dataset is:

{'id': 12305,

'width': 680,

'height': 1024,

'file_name': '064c8022b32931e787260d81ed5aafe8.jpg',

'license': 4,

'time_captured': 'March-August, 2018',

'original_url': 'https://farm2.staticflickr.com/1936/8607950470_9d9d76ced7_o.jpg',

'isstatic': 1,

'kaggle_id': '064c8022b32931e787260d81ed5aafe8'}

NOTE: Not all the images in the FashionPedia dataset have the correponding skin mask in the visuAAL Skin Segmentation Dataset, as there are images in which only garment parts and not people are present in them. These images were removed when creating the visuAAL Skin Segmentation Dataset. However, all the instances in the visuAAL skin segmentation dataset have their corresponding match in the FashionPedia dataset.

Skin Cancer Segmentation

## Overview

Skin Cancer Segmentation is a dataset for object detection tasks - it contains Akiec Bcc Df Nv Vas Mel Bkl annotations for 2,226 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Concerning the four selected segments, the segment Face has the largest revenue with 113.72 billion U.S. dollars. Contrastingly, Baby & Child is ranked last, with 4.86 billion U.S. dollars. Their difference, compared to Face, lies at 108.86 billion U.S. dollars. Find other insights concerning similar markets and segments, such as a ranking of subsegments in the Philippines regarding average revenue per user (ARPU) in the segment Personal Care and a ranking of subsegments in the Philippines regarding revenue in the segment Personal Care . The Statista Market Insights cover a broad range of additional markets.

This study uses an in-house two-class dataset comprising 1000 texture images, 500 of which denote human skin texture and the rest represent non-skin textures with high degree of similarity to human skin. These skin texture images were taken by our research team using a DSLR camera to maintain the fine skin texture patterns. Images were captured from various ethnic groups and skin color tones to avoid biasness toward any ethnic groups or skin color. Since different human body parts have different skin texture characteristics, texture images in our dataset were collected from various body parts such as face, leg, core, arms with relatively equal amount of contribution in dataset. In order to better simulate the skin detection challenges in real world and improve the robustness and reliability of our experiments, skin texture images were collected in various scales, direction and lighting conditions. The remaining 500 texture images which denote non-skin textures with high degree of similarity to human skin were collected from various online image repositories. These images, represent the texture of wooden surfaces, sand and other objects such as rugs, animal furs and fabric which are highly similar to actual skin texture, color and hue. The dataset which prepared in this study simulates challenging real-world scenarios to evaluate and compare texture analysis techniques performance in challenging conditions. All texture patches were captured and stored in uncompressed Tagged Image File Format (TIFF) to avoid any alteration or compromise in actual texture patterns. Moreover, any kind of color alternation or image enhancement were avoided. All texture images were manually resized to 150x150 dimension to equalize the amount of contribution of each image in the model.

Skin Segmentation

## Overview

Skin Segmentation is a dataset for instance segmentation tasks - it contains Objects annotations for 2,500 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Skin Lesion Segmentation

## Overview

Skin Lesion Segmentation is a dataset for instance segmentation tasks - it contains Affected_area annotations for 256 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

🔗 Usage with 🤗 Datasets Library

==============================================================================

Final and reliable method — clean dataset structure, no .cast() required

==============================================================================

Step 1: Install the Hugging Face datasets library

!pip install datasets -q

Step 2: Download and unzip the dataset (recommended method)

import requests from zipfile import ZipFile from io import BytesIO from… See the full description on the dataset page: https://huggingface.co/datasets/makhresearch/skin-lesion-segmentation-classification.

Skin Cancer Segmentation

## Overview

Skin Cancer Segmentation is a dataset for semantic segmentation tasks - it contains Skin Cancer annotations for 9,998 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Over the last two observations, the revenue is forecast to significantly increase in all segments. As part of the positive trend, the revenue achieves the maximum value across all four different segments by the end of the comparison period. Notably, the segment Body stands out with the highest value of ****** million U.S. dollars. Find other insights concerning similar markets and segments, such as a comparison of revenue in Italy and a comparison of average revenue per user (ARPU) in the Philippines.The Statista Market Insights cover a broad range of additional markets.

Skin lesion segmentation performance results of networks, achieved on the ISIC 2017 dataset (based on experiments).

Skin Disease Segmentation

## Overview

Skin Disease Segmentation is a dataset for instance segmentation tasks - it contains Skin Disease annotations for 200 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Lesion Boundary Segmentation Dataset is a dataset for lesion segmentation from the ISIC2018 challenge. The dataset contains skin lesions and their corresponding annotations.

In 2024, multi-outlet skin care sales in the United States were highest for facial cleansers. In that year, this segment generated approximately **** billion U.S. dollars in sales. Facial anti-aging products were the ****** most sought-after kind of skin care treatment, with sales amounting to over *********** U.S. dollars in the same period. Face care: the leading category When thinking about skin care, the first thing that comes to mind is most likely facial care products, such as creams, moisturizers, and lotions. In 2024, the skin care market was worth over ** billion U.S. dollars in the United States alone, and the face care segment made up most of it. Globally, the value of the cosmetics category was forecast to increase by 2030. The impact of younger generations Skin care routines have recently become a trend, especially thanks to social media platforms. It is not only about reducing wrinkles and anti-aging effects: younger generations are interested in skin care too. It is not uncommon for consumers to search for the best skin care tips and treatments, or to buy several products of this kind online. An increasing interest in Korean beauty, with its 10-step-skin care-routine, has also been observed, especially among Gen Z. At the same time, Gen Alpha is entering the market, showing interest in several categories.

The global market size of Cosmetic Skin Care is $XX million in 2018 with XX CAGR from 2014 to 2018, and it is expected to reach $XX million by the end of 2024 with a CAGR of XX% from 2019 to 2024.
Global Cosmetic Skin Care Market Report 2019 - Market Size, Share, Price, Trend and Forecast is a professional and in-depth study on the current state of the global Cosmetic Skin Care industry. The key insights of the report:
1.The report provides key statistics on the market status of the Cosmetic Skin Care manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the industry.
2.The report provides a basic overview of the industry including its definition, applications and manufacturing technology.
3.The report presents the company profile, product specifications, capacity, production value, and 2013-2018 market shares for key vendors.
4.The total market is further divided by company, by country, and by application/type for the competitive landscape analysis.
5.The report estimates 2019-2024 market development trends of Cosmetic Skin Care industry.
6.Analysis of upstream raw materials, downstream demand, and current market dynamics is also carried out
7.The report makes some important proposals for a new project of Cosmetic Skin Care Industry before evaluating its feasibility.
There are 4 key segments covered in this report: competitor segment, product type segment, end use/application segment and geography segment.
For competitor segment, the report includes global key players of Cosmetic Skin Care as well as some small players.
The information for each competitor includes:
* Company Profile
* Main Business Information
* SWOT Analysis
* Sales, Revenue, Price and Gross Margin
* Market Share

For product type segment, this report listed main product type of Cosmetic Skin Care market
* Product Type I
* Product Type II
* Product Type III

For end use/application segment, this report focuses on the status and outlook for key applications. End users sre also listed.
* Online Sales
* Standalone Retail Outlets
* Factory Outlet
* Supermarkets

For geography segment, regional supply, application-wise and type-wise demand, major players, price is presented from 2013 to 2023. This report covers following regions:
* North America
* South America
* Asia & Pacific
* Europe
* MEA (Middle East and Africa)
The key countries in each region are taken into consideration as well, such as United States, China, Japan, India, Korea, ASEAN, Germany, France, UK, Italy, Spain, CIS, and Brazil etc.

Reasons to Purchase this Report:
* Analyzing the outlook of the market with the recent trends and SWOT analysis
* Market dynamics scenario, along with growth opportunities of the market in the years to come
* Market segmentation analysis including qualitative and quantitative research incorporating the impact of economic and non-economic aspects
* Regional and country level analysis integrating the demand and supply forces that are influencing the growth of the market.
* Market value (USD Million) and volume (Units Million) data for each segment and sub-segment
* Competitive landscape involving the market share of major players, along with the new projects and strategies adopted by players in the past five years
* Comprehensive company profiles covering the product offerings, key financial information, recent developments, SWOT analysis, and strategies employed by the major market players
* 1-year analyst support, along with the data support in excel format.
We also can offer customized report to fulfill special requirements of our clients. Regional and Countries report can be provided as well.

Reflectance confocal microscopy (RCM) is a powerful tool for in-vivo examination of a variety of skin diseases. However, current use of RCM depends on qualitative examination by a human expert to look for specific features in the different strata of the skin. Developing approaches to quantify features in RCM imagery requires an automated understanding of what anatomical strata is present in a given en-face section. This work presents an automated approach using a bag of features approach to represent en-face sections and a logistic regression classifier to classify sections into one of four classes (stratum corneum, viable epidermis, dermal-epidermal junction and papillary dermis). This approach was developed and tested using a dataset of 308 depth stacks from 54 volunteers in two age groups (20–30 and 50–70 years of age). The classification accuracy on the test set was 85.6%. The mean absolute error in determining the interface depth for each of the stratum corneum/viable epidermis, vi...

Skin Lesion Segmentation (ISIC 2019)

## Overview

Skin Lesion Segmentation (ISIC 2019) is a dataset for object detection tasks - it contains Affected_area annotations for 256 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Context

Dermatoscopic images usually depict a single skin lesion, but large scale datasets with available segmentations of affected areas are not available until now. Challenge segmentation data often suffered from being either too coarse or too noisy. This dataset provides 10015 binary segmentation masks based on FCN-created segmentations and hand-drawn lines, which together with the HAM10000 diagnosis metadata can be used for object detection or semantic segmentation.

Content

This dataset contains binary segmentation masks as PNG-files of all HAM10000 dataset images. The area segments lesion area as evaluated by a single dermatologist (me). They were initiated with a FCN lesion segmentation model, where afterwards I went through all of them and either approved them, or corrected / redrew them with the free-hand selection tool in FIJI.

You can find the HAM10000 dataset images at the following places: - Harvard Dataverse: https://doi.org/10.7910/DVN/DBW86T - ISIC Archive Gallery: https://www.isic-archive.com - Kaggle Dataset Kernel (downsampled): https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000

Acknowledgements

If you use this data, please cite/refer to the publication I made these segmentation masks for...

• Tschandl, P., Rinner, C., Apalla, Z. et al. Human–computer collaboration for skin cancer recognition. Nat Med (2020). https://doi.org/10.1038/s41591-020-0942-0

...and the original source of the images:

• Tschandl, P., Rosendahl, C. & Kittler, H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5, 180161 (2018). https://doi.org/10.1038/sdata.2018.161

The revenue in the 'Skin Care' segment of the beauty & personal care market in France was forecast to continuously increase between 2025 and 2030 by in total 0.6 billion U.S. dollars (+11.01 percent). After the eighth consecutive increasing year, the revenue is estimated to reach 6.05 billion U.S. dollars and therefore a new peak in 2030. Find further information concerning the revenue in the 'Cosmetics' segment of the beauty & personal care market in Japan and the average revenue per capita in the 'Personal Care' segment of the beauty & personal care market in New Zealand. The Statista Market Insights cover a broad range of additional markets.

Comparative experiences with state-of-the-art methods on fused networks.

The global market size of Medicine Skin Care Products is $XX million in 2018 with XX CAGR from 2014 to 2018, and it is expected to reach $XX million by the end of 2024 with a CAGR of XX% from 2019 to 2024.
Global Medicine Skin Care Products Market Report 2019 - Market Size, Share, Price, Trend and Forecast is a professional and in-depth study on the current state of the global Medicine Skin Care Products industry. The key insights of the report:
1.The report provides key statistics on the market status of the Medicine Skin Care Products manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the industry.
2.The report provides a basic overview of the industry including its definition, applications and manufacturing technology.
3.The report presents the company profile, product specifications, capacity, production value, and 2013-2018 market shares for key vendors.
4.The total market is further divided by company, by country, and by application/type for the competitive landscape analysis.
5.The report estimates 2019-2024 market development trends of Medicine Skin Care Products industry.
6.Analysis of upstream raw materials, downstream demand, and current market dynamics is also carried out
7.The report makes some important proposals for a new project of Medicine Skin Care Products Industry before evaluating its feasibility.
There are 4 key segments covered in this report: competitor segment, product type segment, end use/application segment and geography segment.
For competitor segment, the report includes global key players of Medicine Skin Care Products as well as some small players.
The information for each competitor includes:
* Company Profile
* Main Business Information
* SWOT Analysis
* Sales, Revenue, Price and Gross Margin
* Market Share

For product type segment, this report listed main product type of Medicine Skin Care Products market
* Product Type I
* Product Type II
* Product Type III

For end use/application segment, this report focuses on the status and outlook for key applications. End users sre also listed.
* Application I
* Application II
* Application III

For geography segment, regional supply, application-wise and type-wise demand, major players, price is presented from 2013 to 2023. This report covers following regions:
* North America
* South America
* Asia & Pacific
* Europe
* MEA (Middle East and Africa)
The key countries in each region are taken into consideration as well, such as United States, China, Japan, India, Korea, ASEAN, Germany, France, UK, Italy, Spain, CIS, and Brazil etc.

Reasons to Purchase this Report:
* Analyzing the outlook of the market with the recent trends and SWOT analysis
* Market dynamics scenario, along with growth opportunities of the market in the years to come
* Market segmentation analysis including qualitative and quantitative research incorporating the impact of economic and non-economic aspects
* Regional and country level analysis integrating the demand and supply forces that are influencing the growth of the market.
* Market value (USD Million) and volume (Units Million) data for each segment and sub-segment
* Competitive landscape involving the market share of major players, along with the new projects and strategies adopted by players in the past five years
* Comprehensive company profiles covering the product offerings, key financial information, recent developments, SWOT analysis, and strategies employed by the major market players
* 1-year analyst support, along with the data support in excel format.
We also can offer customized report to fulfill special requirements of our clients. Regional and Countries report can be provided as well.