论文标题
HSADML：针对脑肿瘤分类的超球形深度度量学习
HSADML: Hyper-Sphere Angular Deep Metric based Learning for Brain Tumor Classification
论文作者
论文摘要
脑肿瘤是穿透大脑区域的聚类细胞的异常质量。他们及时的识别和分类有助于医生提供适当的治疗方法。然而，由于高间隔类的相似性和较低的类别的变异性，脑肿瘤的分类非常复杂。由于不同类别的各种MRI分节之间的形态相似性，挑战会加深。这一切都导致了分类模型的普遍性。为此，本文提出了HSADML，这是一个新型框架，可以使用圆顶损失实现深度度量学习（DML）。距离损失将特征嵌入到超晶状体中，然后在嵌入式上施加边距，以增强类之间的可不同性。通过利用圆顶损失的基于深度度量学习，可以确保在将不同的样本聚集在一起的同时将不同的样本分开。结果反映了该方法中的促销活动，提出的框架实现了使用K-NN（K = 1）的最新验证验证的验证验证（K = 1），并且这显着高于正常的SoftMax损失训练，尽管它获得了98.47％的验证精度，但在有限的层间可分离性和内部内部近距离的验证精度上也有限。对各种分类器和损耗函数设置进行的实验分析表明，该方法的潜力。
Brain Tumors are abnormal mass of clustered cells penetrating regions of brain. Their timely identification and classification help doctors to provide appropriate treatment. However, Classifi-cation of Brain Tumors is quite intricate because of high-intra class similarity and low-inter class variability. Due to morphological similarity amongst various MRI-Slices of different classes the challenge deepens more. This all leads to hampering generalizability of classification models. To this end, this paper proposes HSADML, a novel framework which enables deep metric learning (DML) using SphereFace Loss. SphereFace loss embeds the features into a hyperspheric-manifold and then imposes margin on the embeddings to enhance differentiability between the classes. With utilization of SphereFace loss based deep metric learning it is ensured that samples from class clustered together while the different ones are pushed apart. Results reflects the promi-nence in the approach, the proposed framework achieved state-of-the-art 98.69% validation accu-racy using k-NN (k=1) and this is significantly higher than normal SoftMax Loss training which though obtains 98.47% validation accuracy but that too with limited inter-class separability and intra-class closeness. Experimental analysis done over various classifiers and loss function set-tings suggests potential in the approach.
