论文标题
使用BigTransfer（位）对黑素细胞瘤的分类
Classification of Melanocytic Nevus Images using BigTransfer (BiT)
论文作者
论文摘要
皮肤癌是一种致命的疾病，每年对人类的生活造成巨大的损失。彩色皮肤图像在不同的皮肤病变（例如黑色素瘤和柳树）之间显示出很大程度的相似度，从而使鉴定和诊断更具挑战性。黑素细胞NEVI可能会导致致命的黑色素瘤。因此，当前的管理协议涉及去除那些看起来令人生畏的NEVI。但是，这需要有弹性的分类范例来对良性和恶性黑色素细胞NEVI进行分类。早期诊断需要一个可靠的自动化系统，用于黑素细胞NEVI分类，以使诊断有效，及时且成功。在给定的研究中提出了自动分类算法。在此技术中利用了先前在单独的问题陈述上进行过的神经网络，用于对黑素细胞瘤图像进行分类。建议的方法使用BigTransfer（BIT），BigTransfer（BIT）是一种基于重新连接的转移学习方法，将黑素细胞NEVI分类为恶性或良性。将获得的结果与当前技术的结果进行了比较，并且新方法的分类速率被证明超过了现有方法的表现。
Skin cancer is a fatal disease that takes a heavy toll over human lives annually. The colored skin images show a significant degree of resemblance between different skin lesions such as melanoma and nevus, making identification and diagnosis more challenging. Melanocytic nevi may mature to cause fatal melanoma. Therefore, the current management protocol involves the removal of those nevi that appear intimidating. However, this necessitates resilient classification paradigms for classifying benign and malignant melanocytic nevi. Early diagnosis necessitates a dependable automated system for melanocytic nevi classification to render diagnosis efficient, timely, and successful. An automated classification algorithm is proposed in the given research. A neural network previously-trained on a separate problem statement is leveraged in this technique for classifying melanocytic nevus images. The suggested method uses BigTransfer (BiT), a ResNet-based transfer learning approach for classifying melanocytic nevi as malignant or benign. The results obtained are compared to that of current techniques, and the new method's classification rate is proven to outperform that of existing methods.
