# Cross-Modal Conditioned Reconstruction for Language-guided Medical Image Segmentation

arXiv: https://arxiv.org/abs/2404.02845 · HTML: https://mlsanity.com/paper/2404.02845/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 59 of its 89 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 30 of 59)

- [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)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://mlsanity.com/paper/2102.03334/) (2021)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [SimMIM: A Simple Framework for Masked Image Modeling](https://mlsanity.com/paper/2111.09886/) (2021)
- [Modeling Context in Referring Expressions](https://mlsanity.com/paper/1608.00272/) (2016)
- [Generation and Comprehension of Unambiguous Object Descriptions](https://mlsanity.com/paper/1511.02283/) (2015)
- [Florence: A New Foundation Model for Computer Vision](https://mlsanity.com/paper/2111.11432/) (2021)
- [VILA: On Pre-training for Visual Language Models](https://mlsanity.com/paper/2312.07533/) (2023)
- [EVA: Exploring the Limits of Masked Visual Representation Learning at Scale](https://mlsanity.com/paper/2211.07636/) (2022)
- [CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval](https://mlsanity.com/paper/2104.08860/) (2021)
- [Contrastive Learning of Medical Visual Representations from Paired Images and Text](https://mlsanity.com/paper/2010.00747/) (2020)
- [FILIP: Fine-grained Interactive Language-Image Pre-Training](https://mlsanity.com/paper/2111.07783/) (2021)
- [Image Segmentation Using Text and Image Prompts](https://mlsanity.com/paper/2112.10003/) (2021)
- [LAVT: Language-Aware Vision Transformer for Referring Image Segmentation](https://mlsanity.com/paper/2112.02244/) (2021)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance](https://mlsanity.com/paper/2204.08583/) (2022)
- [Cross-Modal Self-Attention Network for Referring Image Segmentation](https://mlsanity.com/paper/1904.04745/) (2019)
- [Generating Images with Multimodal Language Models](https://mlsanity.com/paper/2305.17216/) (2023)
- [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021)

## Built on by (most cited first, 28 of 28)

- [MedGround-R1: Advancing Medical Image Grounding via Spatial-Semantic Rewarded Group Relative Policy Optimization](https://mlsanity.com/paper/2507.02994/) (2025)
- [PhysGame: Uncovering Physical Commonsense Violations in Gameplay Videos](https://mlsanity.com/paper/2412.01800/) (2024)
- [A Refer-and-Ground Multimodal Large Language Model for Biomedicine](https://mlsanity.com/paper/2406.18146/) (2024)
- [A Survey of Medical Vision-and-Language Applications and Their Techniques](https://mlsanity.com/paper/2411.12195/) (2024)
- [Unleashing the Potential of Vision-Language Pre-Training for 3D Zero-Shot Lesion Segmentation via Mask-Attribute Alignment](https://mlsanity.com/paper/2410.15744/) (2024)
- [Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments](https://mlsanity.com/paper/2412.13533/) (2024)
- [Instruction-Guided Lesion Segmentation for Chest X-rays with Automatically Generated Large-Scale Dataset](https://mlsanity.com/paper/2511.15186/) (2025)
- [Uncertainty-Aware Vision-Language Segmentation for Medical Imaging](https://mlsanity.com/paper/2602.14498/) (2026)
- [Frequency-domain Multi-modal Fusion for Language-guided Medical Image Segmentation](https://mlsanity.com/paper/2509.19719/) (2025)
- [The End of Reward Engineering: How LLMs Are Redefining Multi-Agent Coordination](https://mlsanity.com/paper/2601.08237/) (2026)
- [Language-guided Scale-aware MedSegmentor for Lesion Segmentation in Medical Imaging](https://mlsanity.com/paper/2408.17347/) (2024)
- [MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images](https://mlsanity.com/paper/2511.12110/) (2025)
- [Localization-Infused Vision-Language Semantic Fusion for Text-Guided Medical Image Segmentation](https://mlsanity.com/paper/2607.16327/) (2026)
- [Dual-Domain Cross-Modal Decoding for Clinical Text-Guided Medical Image Segmentation](https://mlsanity.com/paper/2608.11335/) (2026)
- [Medical Referring Image Segmentation via Next-Token Mask Prediction](https://mlsanity.com/paper/2511.05044/) (2025)
- [TGC-Net: A Structure-Aware and Semantically-Aligned Framework for Text-Guided Medical Image Segmentation](https://mlsanity.com/paper/2512.21135/) (2025)
- [BiCLIP: Bidirectional and Consistent Language-Image Processing for Robust Medical Image Segmentation](https://mlsanity.com/paper/2603.00156/) (2026)
- [Text as Illumination: Spatial Contrastive Retinex Learning for Language-guided Medical Image Segmentation](https://mlsanity.com/paper/2606.27794/) (2026)
- [Decoupling Language Guidance from Backbones for Text-Guided Medical Segmentation](https://mlsanity.com/paper/2607.09481/) (2026)
- [Test-Time Registers as Global Priors for Tokenized Image Generation](https://mlsanity.com/paper/2607.16824/) (2026)
- [DistMedVL: Distributional Vision-Language Alignment for Uncertainty-Aware Medical Image Segmentation](https://mlsanity.com/paper/2608.05683/) (2026)
- [MedPlex: Deep Vision-Language Co-Adaptation for Clinically Grounded Medical Segmentation](https://mlsanity.com/paper/2608.13690/) (2026)
- [TEAMS: Text-prompted spatiotEmporal dual-heAd Mamba Snake](https://mlsanity.com/paper/2608.17421/) (2026)
- [CGSM: Concept-Guided Segmentation Model for Precise Pulmonary Lesion Delineation](https://mlsanity.com/paper/2609.07004/) (2026)
- [0.5%>100%: Bidirectional Reciprocal Learning for Referring Image Segmentation](https://mlsanity.com/paper/2609.24510/) (2026)
- [Multimodal Routing and Region Refinement for Language-Guided Medical Image Segmentation](https://mlsanity.com/paper/2609.28860/) (2026)
- [XFlow: A Workflow Model for Instruction-Guided Lesion Segmentation in Chest X-rays](https://mlsanity.com/paper/2609.34513/) (2026)
- [Text-Guided Medical Image Segmentation via Residual Aggregation and Dense Alignment Representations](https://mlsanity.com/paper/2610.14944/) (2026)

## Abstract (arXiv)

Recent developments underscore the potential of textual information in enhancing learning models for a deeper understanding of medical visual semantics. However, language-guided medical image segmentation still faces a challenging issue. Previous works employ implicit and ambiguous architectures to embed textual information. This leads to segmentation results that are inconsistent with the semantics represented by the language, sometimes even diverging significantly. To this end, we propose a novel cross-modal conditioned Reconstruction for Language-guided Medical Image Segmentation (RecLMIS) to explicitly capture cross-modal interactions, which assumes that well-aligned medical visual features and medical notes can effectively reconstruct each other. We introduce conditioned interaction to adaptively predict patches and words of interest. Subsequently, they are utilized as conditioning factors for mutual reconstruction to align with regions described in the medical notes. Extensive experiments demonstrate the superiority of our RecLMIS, surpassing LViT by 3.74% mIoU on the publicly available MosMedData+ dataset and achieving an average increase of 1.89% mIoU for cross-domain tests on our QATA-CoV19 dataset. Simultaneously, we achieve a relative reduction of 20.2% in parameter count and a 55.5% decrease in computational load. The code will be available at https://github.com/ShashankHuang/RecLMIS.