论文标题
探索在小数据集上进行变形金刚和CNN的进展
Exploring Advances in Transformers and CNN for Skin Lesion Diagnosis on Small Datasets
论文作者
论文摘要
皮肤癌是世界上最常见的癌症类型之一。已经提出了不同的计算机辅助诊断系统来解决皮肤病变诊断，其中大多数基于深度卷积神经网络。但是，计算机视觉的最新进展实现了最先进的过程，导致许多任务，尤其是基于变压器的网络。我们探索和评估用于皮肤病变诊断任务的计算机视觉架构，训练方法和多模式融合的进步。实验表明，PIT（0.800 \ pm 0.006 $），外套（$ 0.780 \ pm 0.024 $）和VIT（$ 0.771 \ pm 0.018 $）的骨架型号具有Metablock Fusion，可在PAD-IFES-20数据集中获得最先进的ART型型型均取得最先进的结果。
Skin cancer is one of the most common types of cancer in the world. Different computer-aided diagnosis systems have been proposed to tackle skin lesion diagnosis, most of them based in deep convolutional neural networks. However, recent advances in computer vision achieved state-of-art results in many tasks, notably Transformer-based networks. We explore and evaluate advances in computer vision architectures, training methods and multimodal feature fusion for skin lesion diagnosis task. Experiments show that PiT ($0.800 \pm 0.006$), CoaT ($0.780 \pm 0.024$) and ViT ($0.771 \pm 0.018$) backbone models with MetaBlock fusion achieved state-of-art results for balanced accuracy metric in PAD-UFES-20 dataset.
