# AliFuse: Aligning and Fusing Multi-modal Medical Data for Computer-Aided Diagnosis

arXiv: https://arxiv.org/abs/2401.01074 · HTML: https://mlsanity.com/paper/2401.01074/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 31 references that are in the map.
- First posted on arXiv 2 Jan 2024.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)
- [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021)
- [MedCLIP: Contrastive Learning from Unpaired Medical Images and Text](https://mlsanity.com/paper/2210.10163/) (2022)
- [Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning](https://mlsanity.com/paper/2203.02053/) (2022)
- [GIT: A Generative Image-to-text Transformer for Vision and Language](https://mlsanity.com/paper/2205.14100/) (2022)
- [VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts](https://mlsanity.com/paper/2111.02358/) (2021)
- [PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering](https://mlsanity.com/paper/2305.10415/) (2023)
- [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019)
- [Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training](https://mlsanity.com/paper/2105.11333/) (2021)
- [A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics](https://mlsanity.com/paper/2306.00864/) (2023)
- [Heterogeneous Graph Learning for Multi-modal Medical Data Analysis](https://mlsanity.com/paper/2211.15158/) (2022)
- [Mining Gaze for Contrastive Learning toward Computer-Assisted Diagnosis](https://mlsanity.com/paper/2312.06069/) (2023)

## Built on by (most cited first, 7 of 7)

- [Brain-WM: Brain Glioblastoma World Model](https://mlsanity.com/paper/2603.07562/) (2026)
- [HoloDx: Knowledge- and Data-Driven Multimodal Diagnosis of Alzheimer's Disease](https://mlsanity.com/paper/2504.19075/) (2025)
- [Towards Multimodal Active Learning: Efficient Learning with Limited Paired Data](https://mlsanity.com/paper/2510.03247/) (2025)
- [EMAD: Evidence-Centric Grounded Multimodal Diagnosis for Alzheimer's Disease](https://mlsanity.com/paper/2602.19178/) (2026)
- [AD-Reasoning: Multimodal Guideline-Guided Reasoning for Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2603.24059/) (2026)
- [AMF-MedIT: An Efficient Align-Modulation-Fusion Framework for Medical Image-Tabular Data](https://mlsanity.com/paper/2506.19439/) (2025)
- [Quasi-multimodal-based pathophysiological feature learning for retinal disease diagnosis](https://mlsanity.com/paper/2602.03622/) (2026)

## Abstract (arXiv)

Medical data collected for diagnostic decisions are typically multimodal, providing comprehensive information on a subject. While computer-aided diagnosis systems can benefit from multimodal inputs, effectively fusing such data remains a challenging task and a key focus in medical research. In this paper, we propose a transformer-based framework, called Alifuse, for aligning and fusing multimodal medical data. Specifically, we convert medical images and both unstructured and structured clinical records into vision and language tokens, employing intramodal and intermodal attention mechanisms to learn unified representations of all imaging and non-imaging data for classification. Additionally, we integrate restoration modeling with contrastive learning frameworks, jointly learning the high-level semantic alignment between images and texts and the low-level understanding of one modality with the help of another. We apply Alifuse to classify Alzheimer's disease, achieving state-of-the-art performance on five public datasets and outperforming eight baselines.