论文标题
可视化大衣预测用于帮助黑色素瘤检测
Visualizing CoAtNet Predictions for Aiding Melanoma Detection
论文作者
论文摘要
黑色素瘤被认为是皮肤癌最具侵略性的形式。由于恶性和良性癌性病变的形状相似，医生在诊断这些发现时花费了更多的时间。目前，恶性肿瘤的评估主要是通过对可疑病变的侵入性组织学检查进行的。为早期有效检测开发准确的分类器可以最大程度地减少和监测皮肤癌的有害影响并提高患者的存活率。本文提出了使用CoatNet Architecture的多级分类任务，GoatNet体系结构是一种混合模型，将传统卷积神经网络的深度卷积矩阵操作与变压器模型和自我注意力的力学的优势相结合，以实现更好的概括和能力。拟议的多级分类器的总体精度为0.901，召回0.895和AP 0.923，表明与其他最先进的网络相比，高性能表明高性能。
Melanoma is considered to be the most aggressive form of skin cancer. Due to the similar shape of malignant and benign cancerous lesions, doctors spend considerably more time when diagnosing these findings. At present, the evaluation of malignancy is performed primarily by invasive histological examination of the suspicious lesion. Developing an accurate classifier for early and efficient detection can minimize and monitor the harmful effects of skin cancer and increase patient survival rates. This paper proposes a multi-class classification task using the CoAtNet architecture, a hybrid model that combines the depthwise convolution matrix operation of traditional convolutional neural networks with the strengths of Transformer models and self-attention mechanics to achieve better generalization and capacity. The proposed multi-class classifier achieves an overall precision of 0.901, recall 0.895, and AP 0.923, indicating high performance compared to other state-of-the-art networks.
