MULTI-VIEW CONTRASTIVE LEARNING WITH ADDITIVE MARGIN FOR ADAPTIVE NASOPHARYNGEAL CARCINOMA RADIOTHERAPY PREDICTION Jiabao Sheng12 Yuanpeng Zhang3 Jing Cai12 Sai-Kit Lam12 Zhe Li1 Jiang Zhang1 Xinzhi Teng1

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MULTI-VIEW CONTRASTIVE LEARNING WITH ADDITIVE MARGIN FOR ADAPTIVE

NASOPHARYNGEAL CARCINOMA RADIOTHERAPY PREDICTION

Jiabao Sheng1,2, Yuanpeng Zhang∗3, Jing Cai∗1,2, Sai-Kit Lam1,2, Zhe Li 1, Jiang Zhang1, Xinzhi Teng1

1The Hong Kong Polytechnic University

2Research Institute for Smart Ageing, The Hong Kong Polytechnic University

3Department of Medical Informatics, Nantong University

ABSTRACT

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 sur-

vival of patients. Currently, due to the complex tumor micro-

environment, a single type of high-resolution image can pro-

vide only limited information. Meanwhile, the traditional

softmax-based loss is insufﬁcient for quantifying the discrim-

inative 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 embed-

ding space is learned by means of contrastive learning. NPC

samples from the same patient or with similar labels will re-

main close in the embedding space, while NPC samples with

different labels will be far apart. To improve the discrimina-

tive 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.

Index Terms—Medical Image Analysis, Multi-view,

Nasopharyngeal Carcinoma, Contrastive Learning, Additive

Margin

1. INTRODUCTION

Planning intensity-modulated radiotherapy (IMRT) for

NPC requires medical imaging guidance. Previous studies

have shown that the target volume (TV) and organ-at-risk

(OAR) geometry appearing in images can change signiﬁ-

cantly during IMRT [1, 2]. To reduce unnecessary exposure

This work was supported in part by Shenzhen-Hong Kong-Macau S&T

Program (Category C) (SGDX20201103095002019), Shenzhen Basic Re-

search Program (JCYJ20210324130209023) of Shenzhen Science and Tech-

nology Innovation Committee, Project of Strategic Importance (P0035421),

Project of RISA (P0043001) of The Hong Kong Polytechnic University,

the NSF of Jiangsu Province (No. BK20201441), Jiangsu Post-doctoral

Research Funding Program (No. 2020Z020), and the NSFC (Grant No.

82072019).

embedding

Space

𝑣1

𝑖∈ 𝑉

1𝑣2

𝑖∈ 𝑉

2𝑣3

𝑖∈ 𝑉

3𝑣4

𝑖∈ 𝑉

positive samples

Encoder Encoder Encoder Encoder

𝑍1

𝑖𝑍2

𝑖𝑍3

𝑖𝑍4

𝑖

query

samples

𝑣1

𝑗∈ 𝑉

1𝑣2

𝑗∈ 𝑉

𝑣3

𝑗∈ 𝑉

3𝑣4

𝑗∈ 𝑉

negative

samples

Encoder

𝑍1,2,3,4

𝑗

target

region

target

region

Fig. 1: Illustration of our basic idea. V1,V2,V3, and V4are

different medical image views in the NPC-GTV dataset. vi

and vjare different patients associated with NPC samples.

ziand zjrepresent vectors. Our objective is to learn an em-

bedding space in which similar sample pairs stay close while

dissimilar ones are far apart.

during treatment, it is necessary to incorporate medical im-

age analysis to assist doctors in evaluating whether ART is

needed.

In contrast to other medical image classiﬁcation tasks,

such as tumour identiﬁcation [3, 4], and cancer diagnosis [5,

6], the prediction task for NPC ART is to analyze the proper-

ties of tumour to distinguish the need for radiotherapy replan-

ning in the short term. Due to the heterogeneity of tumours

[7, 8], the volume, shape, and texture of the tumour region

may vary from patient to patient, and many diverse factors

may cause these features to change.

In a previous study, [9] used artiﬁcially extracted mag-

netic resonance imaging (MRI) features to study radiation

therapy planning for NPC. However, a single manually ex-

tracted omics signature cannot fully express the information

of NPC samples [10]. The sample learning methods used

in many studies [10] showed that manually extracted multi-

omics feature representation data could not be obtained better

arXiv:2210.15201v1 [cs.CV] 27 Oct 2022

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MULTI-VIEWCONTRASTIVELEARNINGWITHADDITIVEMARGINFORADAPTIVENASOPHARYNGEALCARCINOMARADIOTHERAPYPREDICTIONJiabaoSheng1;2,YuanpengZhang3,JingCai1;2,Sai-KitLam1;2,ZheLi1,JiangZhang1,XinzhiTeng11TheHongKongPolytechnicUniversity2ResearchInstituteforSmartAgeing,TheHongKongPolytechnicUniversity3Departmento...

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MULTI-VIEW CONTRASTIVE LEARNING WITH ADDITIVE MARGIN FOR ADAPTIVE NASOPHARYNGEAL CARCINOMA RADIOTHERAPY PREDICTION Jiabao Sheng12 Yuanpeng Zhang3 Jing Cai12 Sai-Kit Lam12 Zhe Li1 Jiang Zhang1 Xinzhi Teng1

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