This study leverages graph neural networks to integrate MELC data with
Radiomic-extracted features for melanoma classification, focusing on cell-wise
analysis. It assesses the effectiveness of gene expression profiles and
Radiomic features, revealing that Radiomic features, particularly when combined
with UMAP for dimensionality reduction, significantly enhance classification
performance. Notably, using Radiomics contributes to increased diagnostic
accuracy and computational efficiency, as it allows for the extraction of
critical data from fewer stains, thereby reducing operational costs. This
methodology marks an advancement in computational dermatology for melanoma cell
classification, setting the stage for future research and potential
developments.

该研究利用图神经网络将 MELC 数据与放射学特征相结合，着重进行细胞级别分析，评估基因表达谱和放射学特征的有效性，发现放射学特征，尤其是与 UMAP 结合进行降维，显著提高分类性能。值得注意的是，使用放射学特征有助于提高诊断准确性和计算效率，因为它能够从较少的染色中提取关键数据，从而降低操作成本。这种方法在黑色素瘤细胞分类的计算皮肤病学中标志着一项进展，为未来的研究和潜在的发展奠定了基础。

基于图神经网络的组织病理学数据中放射组学特征和基因表达谱的比较分析

Comparative Analysis of Radiomic Features and Gene Expression Profiles  in Histopathology Data Using Graph Neural Networks

Convolutional neural networks (CNNs) deliver exceptional results for computer
vision, including medical image analysis. With the growing number of available
architectures, picking one over another is far from obvious. Existing art
suggests that, when performing transfer learning, the performance of CNN
architectures on ImageNet correlates strongly with their performance on target
tasks. We evaluate that claim for melanoma classification, over 9 CNNs
architectures, in 5 sets of splits created on the ISIC Challenge 2017 dataset,
and 3 repeated measures, resulting in 135 models. The correlations we found
were, to begin with, much smaller than those reported by existing art, and
disappeared altogether when we considered only the top-performing networks:
uncontrolled nuisances (i.e., splits and randomness) overcome any of the
analyzed factors. Whenever possible, the best approach for melanoma
classification is still to create ensembles of multiple models. We compared two
choices for selecting which models to ensemble: picking them at random (among a
pool of high-quality ones) vs. using the validation set to determine which ones
to pick first. For small ensembles, we found a slight advantage on the second
approach but found that random choice was also competitive. Although our aim in
this paper was not to maximize performance, we easily reached AUCs comparable
to the first place on the ISIC Challenge 2017.

本研究在 ISIC Challenge 2017 数据集上对 9 种 CNN 架构进行了 5 种切分和 3 种重复测试，结果发现 CNN 架构在 ImageNet 上的表现与目标任务的表现强相关性的观点不太成立，当考虑仅使用表现最好的网络时，相关性消失，并且最佳方法仍然是创建多个模型的集成。本研究还比较了两种选择模型的方法，并发现使用验证集进行挑选略优于随机选择。

选择 CNN 结构进行黑色素瘤分类：单独还是集成？

Solo or Ensemble? Choosing a CNN Architecture for Melanoma  Classification

Deep learning models show remarkable results in automated skin lesion
analysis. However, these models demand considerable amounts of data, while the
availability of annotated skin lesion images is often limited. Data
augmentation can expand the training dataset by transforming input images. In
this work, we investigate the impact of 13 data augmentation scenarios for
melanoma classification trained on three CNNs (Inception-v4, ResNet, and
DenseNet). Scenarios include traditional color and geometric transforms, and
more unusual augmentations such as elastic transforms, random erasing and a
novel augmentation that mixes different lesions. We also explore the use of
data augmentation at test-time and the impact of data augmentation on various
dataset sizes. Our results confirm the importance of data augmentation in both
training and testing and show that it can lead to more performance gains than
obtaining new images. The best scenario results in an AUC of 0.882 for melanoma
classification without using external data, outperforming the top-ranked
submission (0.874) for the ISIC Challenge 2017, which was trained with
additional data.

本文探讨了 13 种数据增强下三种卷积神经网络（Inception-v4、ResNet 和 DenseNet）对黑色素瘤分类的影响，结果表明数据增强可以在训练和测试中都提高性能，且可以比获得新图像更有效。最佳情况下，不借助外部数据的黑色素瘤分类 AUC 为 0.882，超过了使用附加数据进行训练的 ISIC Challenge 2017 的最佳结果（0.874）。