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This paper presents the results of prediction of therapeutic responses for patients who undergo chemo or radio therapy for the treatment of brain tumour. This work is in response to Task 11 of 2025 BraTS Brain Tumor Progression Challenge organized in conjunction with MICCAI 2025 ( https://conferences.miccai.org/2025/en/ ). In this competition, a Siamese Vision Transformer (SViT) is applied, which allows the inferences between baseline state, i.e. after tumour resection or initial treatment and later tumour development. The backbone model is CMT-Ti. Inspired by the work of MuSiC_ViT for x-ray chest disease detection, this SViT system accomplishes four classification of therapeutic responses, which are Complete Response (CR), Partial Response (PR), Stable Disease (SD) and Progressive Disease (PD). Overall, based on the available training dataset with 91 patients, 90% accuracy can be achieved. For the test dataset, the model SViT has achieved top 2 performance for Task 11.

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A Siamese Vision Transfer Architecture for Prediction of Brain Tumour Responses during Therapy

  • 会议论文

  • 发表日期：

    2026年

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  • Xiaohong W. Gao,
  • Chia-Hui Chien,
  • Guan-Lin Liu,
  • Jyh-Cheng Chen

摘要

This paper presents the results of prediction of therapeutic responses for patients who undergo chemo or radio therapy for the treatment of brain tumour. This work is in response to Task 11 of 2025 BraTS Brain Tumor Progression Challenge organized in conjunction with MICCAI 2025 ( https://conferences.miccai.org/2025/en/ ). In this competition, a Siamese Vision Transformer (SViT) is applied, which allows the inferences between baseline state, i.e. after tumour resection or initial treatment and later tumour development. The backbone model is CMT-Ti. Inspired by the work of MuSiC_ViT for x-ray chest disease detection, this SViT system accomplishes four classification of therapeutic responses, which are Complete Response (CR), Partial Response (PR), Stable Disease (SD) and Progressive Disease (PD). Overall, based on the available training dataset with 91 patients, 90% accuracy can be achieved. For the test dataset, the model SViT has achieved top 2 performance for Task 11.