Skin cancer is a global health concern, necessitating early and accurate
diagnosis for improved patient outcomes. This study introduces a groundbreaking
approach to skin cancer classification, employing the Vision Transformer, a
state-of-the-art deep learning architecture renowned for its success in diverse
image analysis tasks. Utilizing the HAM10000 dataset of 10,015 meticulously
annotated skin lesion images, the model undergoes preprocessing for enhanced
robustness. The Vision Transformer, adapted to the skin cancer classification
task, leverages the self-attention mechanism to capture intricate spatial
dependencies, achieving superior performance over traditional deep learning
architectures. Segment Anything Model aids in precise segmentation of cancerous
areas, attaining high IOU and Dice Coefficient. Extensive experiments highlight
the model's supremacy, particularly the Google-based ViT patch-32 variant,
which achieves 96.15% accuracy and showcases potential as an effective tool for
dermatologists in skin cancer diagnosis, contributing to advancements in
dermatological practices.

这篇研究论文介绍了一种开创性的方法来进行皮肤癌分类，采用了视觉转换器作为深度学习架构，在精细标注的皮肤病变图像数据集上进行预处理和训练，通过自注意机制捕捉复杂的空间依赖关系，取得优于传统深度学习架构的卓越性能，特别是 Google 基于 ViT patch-32 变体，达到 96.15% 的准确率，为皮肤科医生在皮肤癌诊断中提供了潜力巨大的有效工具。

基于视觉变换器的皮肤癌分割与分类，用于皮肤镜非侵入式数字系统的自动分析

Skin Cancer Segmentation and Classification Using Vision Transformer for  Automatic Analysis in Dermatoscopy-based Non-invasive Digital System

Skin cancer is a prevalent and potentially fatal disease that requires
accurate and efficient diagnosis and treatment. Although manual tracing is the
current standard in clinics, automated tools are desired to reduce human labor
and improve accuracy. However, developing such tools is challenging due to the
highly variable appearance of skin cancers and complex objects in the
background. In this paper, we present SkinSAM, a fine-tuned model based on the
Segment Anything Model that showed outstanding segmentation performance. The
models are validated on HAM10000 dataset which includes 10015 dermatoscopic
images. While larger models (ViT_L, ViT_H) performed better than the smaller
one (ViT_b), the finetuned model (ViT_b_finetuned) exhibited the greatest
improvement, with a Mean pixel accuracy of 0.945, Mean dice score of 0.8879,
and Mean IoU score of 0.7843. Among the lesion types, vascular lesions showed
the best segmentation results. Our research demonstrates the great potential of
adapting SAM to medical image segmentation tasks.

本研究基于 HAM10000 数据集，提出 SkinSAM 模型实现皮肤癌图像自动分割，最终模型准确度较高，证明了将该模型应用于医学图像分割任务的巨大潜力。

SkinSAM：采用任意分割模型强化皮肤癌分割

SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Model

Training of neural networks for automated diagnosis of pigmented skin lesions
is hampered by the small size and lack of diversity of available datasets 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. Given this diversity we had to apply different
acquisition and cleaning methods and developed semi-automatic workflows
utilizing specifically trained neural networks. The final dataset consists of
10015 dermatoscopic images which are released as a training set for academic
machine learning purposes and are publicly available through the ISIC archive.
This benchmark dataset can be used for machine learning and for comparisons
with human experts. Cases include a representative collection of all important
diagnostic categories in the realm of pigmented lesions. More than 50% of
lesions have been confirmed by pathology, while the ground truth for the rest
of the cases was either follow-up, expert consensus, or confirmation by in-vivo
confocal microscopy.

利用具有不同种族和不同获取模式的皮肤镜图像，通过不同的采集和清洗方法以及半自动工作流程和特别训练的神经网络，我们成立了 HAM10000 数据集，这个数据集由 10015 个用于学术机器学习目的的皮肤镜图像组成，覆盖养猪场多种重要诊断范畴，用于训练神经网络。