The prediction of adaptive radiation therapy (ART) prior to radiation therapy (RT) for nasopharyngeal carcinoma (NPC) patients is important to reduce toxicity and prolong the survival of patients. Currently, due to the complex tumor micro-environment, a single type of high-resolution image can provide only limited information. Meanwhile, the traditional softmax-based loss is insufficient for quantifying the discriminative power of a model. To overcome these challenges, we propose a supervised multi-view contrastive learning method with an additive margin (MMCon). For each patient, four medical images are considered to form multi-view positive pairs, which can provide additional information and enhance the representation of medical images. In addition, the embedding space is learned by means of contrastive learning. NPC samples from the same patient or with similar labels will remain close in the embedding space, while NPC samples with different labels will be far apart. To improve the discriminative ability of the loss function, we incorporate a margin into the contrastive learning. Experimental result show this new learning objective can be used to find an embedding space that exhibits superior discrimination ability for NPC images.
翻译:在对鼻腔癌病人进行辐射治疗(RT)之前对适应性辐射疗法(ART)的预测对于降低毒性和延长患者存活时间十分重要,目前,由于肿瘤的复杂微环境,单一类型的高分辨率图像只能提供有限的信息。与此同时,传统的软质损失不足以量化模型的歧视性力量。为了克服这些挑战,我们建议采用监督的多视角对比学习方法,并配有添加剂。对于每名患者,四张医学图像被视为形成多视角阳性配对,可以提供补充信息,加强医疗图像的表述。此外,嵌入空间是通过对比学习学习学习而学得的。同一患者或类似标签的NPC样本将留在嵌入空间内,而带有不同标签的NPC样本则相距甚远。为了提高损失功能的歧视性能力,我们将在对比性学习中加入一个比值。实验结果显示,新的学习目标可以用来找到一个嵌入空间,展示NPC图像的超强歧视能力。