ObjectivesTo　evaluate　the　potential　improvement　of　prediction　performance　of　a　proposed　double　branch　multimodality-contribution-aware　TripNet　(MCAT)　in　microvascular　invasion　(MVI)　of　hepatocellular　carcinoma　(HCC)　based　on　a　small　sample.　MethodsIn　this　retrospective　study,　121　HCCs　from　103　consecutive　patients　were　included,　with　44　MVI　positive　and　77　MVI　negative,　respectively.　A　MCAT　model　aiming　to　improve　the　accuracy　of　deep　neural　network　and　alleviate　the　negative　effect　of　small　sample　size　was　proposed　and　the　improvement　of　MCAT　model　was　verified　among　comparisons　between　MCAT　and　other　used　deep　neural　networks　including　2DCNN　(two-dimentional　convolutional　neural　network),　ResNet　(residual　neural　network)　and　SENet　(squeeze-and-excitation　network),　respectively.　ResultsThrough　validation,　the　AUC　value　of　MCAT　is　significantly　higher　than　2DCNN　based　on　CT,　MRI,　and　both　imaging　(P　＜　0.001　for　all).　The　AUC　value　of　model　with　single　branch　pretraining　based　on　small　samples　is　significantly　higher　than　model　with　end-to-end　training　in　CT　branch　and　double　branch　(0.62　vs　0.69,　p=0.016,　0.65　vs　0.83,　p=0.010,　respectively).　The　AUC　value　of　the　double　branch　MCAT　based　on　both　CT　and　MRI　imaging　(0.83)　was　significantly　higher　than　that　of　the　CT　branch　MCAT　(0.69)　and　MRI　branch　MCAT　(0.73)　(P　＜　0.001,　P　=　0.03,　respectively),　which　was　also　significantly　higher　than　common-used　ReNet　(0.67)　and　SENet　(0.70)　model　(P　＜　0.001,　P　=　0.005,　respectively).　ConclusionA　proposed　Double　branch　MCAT　model　based　on　a　small　sample　can　improve　the　effectiveness　in　comparison　to　other　deep　neural　networks　or　single　branch　MCAT　model,　providing　a　potential　solution　for　scenarios　such　as　small-sample　deep　learning　and　fusion　of　multiple　imaging　modalities.