Cancer diagnosis is a well-studied problem in machine learning since early
detection of cancer is often the determining factor in prognosis. Supervised
deep learning achieves excellent results in cancer image classification,
usually through transfer learning. However, these models require large amounts
of labelled data and for several types of cancer, large labelled datasets do
not exist. In this paper, we demonstrate that a model pre-trained using a
self-supervised learning algorithm known as Barlow Twins can outperform the
conventional supervised transfer learning pipeline. We juxtapose two base
models: i) pretrained in a supervised fashion on ImageNet; ii) pretrained in a
self-supervised fashion on ImageNet. Both are subsequently fine tuned on a
small labelled skin lesion dataset and evaluated on a large test set. We
achieve a mean test accuracy of 70\% for self-supervised transfer in comparison
to 66\% for supervised transfer. Interestingly, boosting performance further is
possible by self-supervised pretraining a second time (on unlabelled skin
lesion images) before subsequent fine tuning. This hints at an alternative path
to collecting more labelled data in settings where this is challenging - namely
just collecting more unlabelled images. Our framework is applicable to cancer
image classification models in the low-labelled data regime.

在本文中，我们展示了使用一种称为 Barlow Twins 的自我监督学习算法进行预训练的模型可以胜过常规的监督转移学习流程，该模型在小型标记皮肤病变数据集上进行微调后，在大型测试集中取得了 70% 的平均测试准确率，相比于监督转移学习的 66%。这暗示在标记困难的环境中收集更多未标记图像可能是一条获取更多标记数据的替代途径。我们的框架适用于低标记数据情境下的癌症图像分类模型。

有限训练数据下的自监督学习用于皮肤癌诊断

Self-supervised learning for skin cancer diagnosis with limited training  data

This paper proposes a modified Topological Data Analysis model for skin
images preprocessing and enhancements. The skin lesion dataset HAM10000 used
with the intention of identifying the important objects in relevant regions of
the images. In order to evaluate both the original dataset and the preprocessed
dataset, Deep Convolutional Neural Network and Vision Transformer models were
utilized to train both models. After training, the experimental results
demonstrate that the images preprocessed using the Modified Topological Data
Analysis consistently perform better.

本文提出了一种修改后的拓扑数据分析模型，用于皮肤图像预处理和增强，通过使用皮肤病变数据集 HAM10000 来识别图像相关区域中的重要对象，并利用深度卷积神经网络和视觉变换器模型对原始数据集和预处理数据集进行评估，实验结果表明使用修改后的拓扑数据分析预处理的图像表现出更好的性能。

皮肤病变分类的改进拓扑图像预处理

Modified Topological Image Preprocessing for Skin Lesion Classifications

The recent 'Mpox' outbreak, formerly known as 'Monkeypox', has become a
significant public health concern and has spread to over 110 countries
globally. The challenge of clinically diagnosing mpox early on is due, in part,
to its similarity to other types of rashes. Computer-aided screening tools have
been proven valuable in cases where Polymerase Chain Reaction (PCR) based
diagnosis is not immediately available. Deep learning methods are powerful in
learning complex data representations, but their efficacy largely depends on
adequate training data. To address this challenge, we present the "Mpox Skin
Lesion Dataset Version 2.0 (MSLD v2.0)" as a follow-up to the previously
released openly accessible dataset, one of the first datasets containing mpox
lesion images. This dataset contains images of patients with mpox and five
other non-mpox classes (chickenpox, measles, hand-foot-mouth disease, cowpox,
and healthy). We benchmark the performance of several state-of-the-art deep
learning models, including VGG16, ResNet50, DenseNet121, MobileNetV2,
EfficientNetB3, InceptionV3, and Xception, to classify mpox and other
infectious skin diseases. In order to reduce the impact of racial bias, we
utilize a color space data augmentation method to increase skin color
variability during training. Additionally, by leveraging transfer learning
implemented with pre-trained weights generated from the HAM10000 dataset, an
extensive collection of pigmented skin lesion images, we achieved the best
overall accuracy of $83.59\pm2.11\%$. Finally, the developed models are
incorporated within a prototype web application to analyze uploaded skin images
by a user and determine whether a subject is a suspected mpox patient.

本研究旨在建立包含 Mpox 和其他感染性皮肤病的图像数据集，利用深度学习模型完成图像分类，研究结果表明使用由 HAM10000 数据集预训练的权重来实现迁移学习能够获得最佳的分类准确度。