To address the challenges of training neural networks for automated diagnosis of pigmented skin lesions, the authors introduced the HAM10000 ("Human Against Machine with 10000 training images") dataset. This dataset aimed to overcome the limitations of small-sized and homogeneous dermatoscopic image datasets by providing a diverse and extensive collection. To achieve this, they collected dermatoscopic images from various populations using different modalities, which necessitated employing distinct acquisition and cleaning methods. The authors also designed semi-automatic workflows that incorporated specialized neural networks to enhance the dataset's quality. The resulting HAM10000 dataset comprised 10,015 dermatoscopic images, which were made available for academic machine learning applications through the ISIC archive. This dataset served as a benchmark for machine learning experiments and comparisons with human experts.

The HAM10000 Dataset is a dataset of skin lesion images. It is a popular dataset for skin cancer classification research. The dataset consists of 10,000 images of skin lesions, each of which is labeled with one of seven different types of skin cancer.

Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available dataset of dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human Against Machine with 10000 training images") dataset. We collected dermatoscopic images from different populations, acquired and stored by different modalities. The final dataset consists of 10015 dermatoscopic images which can serve as a training set for academic machine learning purposes. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions: Actinic keratoses and intraepithelial carcinoma / Bowen s disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses, bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv) and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemo

The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

Original Paper and Dataset here Kaggle dataset here

  Introduction to datasets

Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available dataset of dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human Against Machine with 10000 training images") dataset.… See the full description on the dataset page: https://huggingface.co/datasets/marmal88/skin_cancer.

Ham10000 Skin Lesions Annotation

## Overview

Ham10000 Skin Lesions Annotation is a dataset for instance segmentation tasks - it contains Lesions annotations for 298 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).

This dataset is a cleaned and preprocessed combination of the HAM10000 and ISIC Archive dermoscopic image datasets, intended for training and evaluating deep learning models for skin lesion classification.

It is structured to support multi-class image classification, and has been carefully processed to maintain high quality, class balance.

Classes Included :

"melanocytic nevi": "Melanocytic Nevus", "nv": "Melanocytic Nevus", "melanoma": "Melanoma", "mel": "Melanoma", "benign keratosis": "Benign Keratosis", "bkl": "Benign Keratosis", "basal cell carcinoma": "Basal Cell Carcinoma", "bcc": "Basal Cell Carcinoma", "actinic keratosis": "Actinic Keratosis", "akiec": "Actinic Keratosis", "dermatofibroma": "Dermatofibroma", "df": "Dermatofibroma", "vascular lesions": "Vascular Lesion", "vasc": "Vascular Lesion", "warts/molluscum": "Warts/Molluscum"

Preprocessing Notes

• All images resized to 224x224 for CNN compatibility
• Labels unified and cleaned across both datasets
• Invalid or corrupted entries removed

Nagabu/HAM10000 dataset hosted on Hugging Face and contributed by the HF Datasets community

HAM10000

## Overview

HAM10000 is a dataset for object detection tasks - it contains Skin Disease annotations for 1,036 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).

My HAM10000 University

## Overview

My HAM10000 University is a dataset for object detection tasks - it contains Objects annotations for 6,999 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).

augmentation

pranay-43/HAM10000 dataset hosted on Hugging Face and contributed by the HF Datasets community

Reader study was conducted in 3 phases: no ai (phase 1), ai support (phase 2), xai support (phase 3). The data generated in each phase is available in metadata_phase1.csv, metadata_phase2.csv, and metadata_phase3.csv.There are 113 unique participants in phase 1. Additional participants were added in phase 2, resulting in 116 unique clinicians in phases 2 and 3.Important: The 3rd and 13th image in each group are identical. Be careful when performing table joins as the duplicate image_ids can affect them. In metadata_phase1.csv, the AI predictions for the 13th image in each group are null. Please take that into account when performing analysis. In metadata_phase2 and metadata_phase3, the AI predictions for the repeating images are not omitted.participant: Each clinician was assigned a participant Id represented by the participant column.group: Each clinician was randomly assigned to a group. Each group was assigned mutually exclusive sets of images.mask: An internal identifier used for the images. Can be ignored.benign_malignant: ground truth diagnosis.prediction: Diagnosis chosen by the clinician. 1 represents melanoma, 0 represents nevus, 0.5 represents a nevus diagnosis but the clinician chose to excise.confidence: Confidence value entered by the clinician.trust: Trust value entered by the clinician.AI_prediction: Diagnosis predicted by the AI. 1 represents melanoma and 0 represents nevus. language: Language chosen by the clinician.

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

Medical image segmentation is vital to the area of imaging because it enables professionals to more accurately examine and understand the information offered by different imaging modalities.

Dataset

This dataset was created by Henry Herrera007

Contents

Contributors towards pre-processing the existing dataset from the same (dataverse.harvard.edu) platform: Students of College: U.V.Patel College of Engineering, Ganpat University, Gujarat Nishant Darji Kiran Patel Deep Patel Guide: Dr. Yagnesh Vyas During Internship at Bhaskaracharya National Institute for Space Applications and Geo-informatics (BISAG-N) Addressing the challenge of training neural networks for automated diagnosis of pigmented skin lesions, we proudly present the Segmented HAM10000 dataset. Stemming from the original HAM10000, this segmented iteration aims to further enhance the efficacy and diversity of available data for academic machine-learning endeavors. Derived from a diverse array of populations and captured through various modalities, the Segmented HAM10000 dataset boasts a comprehensive collection of 10,015 dermatoscopic images. Each image is meticulously segmented, optimizing it for precise analysis and interpretation by machine learning algorithms. Representing a spectrum of crucial diagnostic categories within pigmented lesions, our dataset encompasses Actinic keratoses and intraepithelial carcinoma / Bowen's disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses, bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv), and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas, and hemorrhage, vasc). More than 50% of lesions in our dataset are confirmed through histopathology (histo), while the ground truth for the remaining cases is established through follow-up examination (follow_up), expert consensus (consensus), or confirmation by in-vivo confocal microscopy (confocal). Notably, the Segmented HAM10000 dataset includes lesions with multiple images, facilitating tracking via the lesion_id-column within the accompanying metadata file.

SkinCanserDetection: HAM10000.v2

## Overview

SkinCanserDetection: HAM10000.v2 is a dataset for object detection tasks - it contains Canser annotations for 8,482 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).

BoooomNing/ham10000 dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Joy Dhar

Contents

Computer-aided diagnosis techniques based on deep learning in skin cancer classification have disadvantages such as unbalanced datasets, redundant information in the extracted features and ignored interactions of partial features among different convolutional layers. In order to overcome these disadvantages, we propose a skin cancer classification model named EFFNet, which is based on feature fusion and random forests. Firstly, the model preprocesses the HAM10000 dataset to make each category of training set images balanced by image enhancement technology. Then, the pre-training weights of the EfficientNetV2 model on the ImageNet dataset are fine-tuned on the HAM10000 skin cancer dataset. After that, an improved hierarchical bilinear pooling is introduced to capture the interactions of some features between the layers and enhance the expressive ability of features. Finally, the fused features are passed into the random forests for classification prediction. The experimental results show that the accuracy, recall, precision and F1-score of the model reach 94.96%, 93.74%, 93.16% and 93.24% respectively. Compared with other models, the accuracy rate is improved to some extent and the highest accuracy rate can be increased by about 10%.