  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.
