mlsanityData to 14 Oct 2026

SAM-I2I: Unleash the Power of Segment Anything Model for Medical Image Translation

arXiv 2411.12755 ↗First posted 13 Nov 2024eess.IV cs.CVcited by 4

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Abstract (arXiv)

Medical image translation is crucial for reducing the need for redundant and expensive multi-modal imaging in clinical field. However, current approaches based on Convolutional Neural Networks (CNNs) and Transformers often fail to capture fine-grain semantic features, resulting in suboptimal image quality. To address this challenge, we propose SAM-I2I, a novel image-to-image translation framework based on the Segment Anything Model 2 (SAM2). SAM-I2I utilizes a pre-trained image encoder to extract multiscale semantic features from the source image and a decoder, based on the mask unit attention module, to synthesize target modality images. Our experiments on multi-contrast MRI datasets demonstrate that SAM-I2I outperforms state-of-the-art methods, offering more efficient and accurate medical image translation.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 12 of its 17 references that are in the map. First posted on arXiv 13 Nov 2024.

Builds on

12 of 17 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2023Segment Anythingcited by 8,234
  5. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  6. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,152
  7. 2024SAM 2: Segment Anything in Images and Videoscited by 2,863
  8. 2023Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistlescited by 290
  9. 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
  10. 2021ResViT: Residual vision transformers for multi-modal medical image synthesiscited by 177
  11. 2021Ensembling Off-the-shelf Models for GAN Trainingcited by 86
  12. 2017200x Low-dose PET Reconstruction using Deep Learningcited by 18

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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025SAM2 for Image and Video Segmentation: A Comprehensive Surveycited by 13
  2. 2025MemorySAM: Memorize Modalities and Semantics with Segment Anything Model 2 for Multi-modal Semantic Segmentationcited by 10
  3. 2025FermatSyn: SAM2-Enhanced Bidirectional Mamba with Isotropic Spiral Scanning for Multi-Modal Medical Image Synthesiscited by 1
  4. 2025Med-K2N: Flexible K-to-N Modality Translation for Medical Image Synthesiscited by 0