ST-Align: A Multimodal Foundation Model for Image-Gene Alignment in Spatial Transcriptomics

• URL: http://arxiv.org/abs/2411.16793v1
• Date: Mon, 25 Nov 2024 09:15:07 GMT
• Title: ST-Align: A Multimodal Foundation Model for Image-Gene Alignment in Spatial Transcriptomics
• Authors: Yuxiang Lin, Ling Luo, Ying Chen, Xushi Zhang, Zihui Wang, Wenxian Yang, Mengsha Tong, Rongshan Yu,
• Abstract summary: Spatial transcriptomics (ST) provides high-resolution pathological images and whole-transcriptomic expression profiles at individual spots across whole-slide scales. ST-Align is the first foundation model designed for ST that deeply aligns image-gene pairs by incorporating spatial context. ST-Align employs specialized encoders tailored to distinct ST contexts, followed by an Attention-Based Fusion Network (ABFN) for enhanced multimodal fusion.
• Score: 6.680197957317297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatial transcriptomics (ST) provides high-resolution pathological images and whole-transcriptomic expression profiles at individual spots across whole-slide scales. This setting makes it an ideal data source to develop multimodal foundation models. Although recent studies attempted to fine-tune visual encoders with trainable gene encoders based on spot-level, the absence of a wider slide perspective and spatial intrinsic relationships limits their ability to capture ST-specific insights effectively. Here, we introduce ST-Align, the first foundation model designed for ST that deeply aligns image-gene pairs by incorporating spatial context, effectively bridging pathological imaging with genomic features. We design a novel pretraining framework with a three-target alignment strategy for ST-Align, enabling (1) multi-scale alignment across image-gene pairs, capturing both spot- and niche-level contexts for a comprehensive perspective, and (2) cross-level alignment of multimodal insights, connecting localized cellular characteristics and broader tissue architecture. Additionally, ST-Align employs specialized encoders tailored to distinct ST contexts, followed by an Attention-Based Fusion Network (ABFN) for enhanced multimodal fusion, effectively merging domain-shared knowledge with ST-specific insights from both pathological and genomic data. We pre-trained ST-Align on 1.3 million spot-niche pairs and evaluated its performance through two downstream tasks across six datasets, demonstrating superior zero-shot and few-shot capabilities. ST-Align highlights the potential for reducing the cost of ST and providing valuable insights into the distinction of critical compositions within human tissue.

Related papers

• Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images [7.048241543461529]
  We propose a novel framework called Multi-Resolution Prompt-guided Hybrid Embedding (MR-PHE) to address these challenges in zero-shot histopathology image classification. We introduce a hybrid embedding strategy that integrates global image embeddings with weighted patch embeddings. A similarity-based patch weighting mechanism assigns attention-like weights to patches based on their relevance to class embeddings.
  arXiv  Detail & Related papers  (2025-03-13T12:18:37Z)
• DELST: Dual Entailment Learning for Hyperbolic Image-Gene Pretraining in Spatial Transcriptomics [38.94542898899791]
  We propose DELST, the first framework to embed hyperbolic representations while modeling hierarchy for image-gene pretraining. Our framework achieves improved predictive performance compared to existing methods.
  arXiv  Detail & Related papers  (2025-03-02T09:00:09Z)
• A Multimodal Approach Combining Structural and Cross-domain Textual Guidance for Weakly Supervised OCT Segmentation [12.948027961485536]
  We propose a novel Weakly Supervised Semantic (WSSS) approach that integrates structural guidance with text-driven strategies to generate high-quality pseudo labels. Our method achieves state-of-the-art performance, highlighting its potential to improve diagnostic accuracy and efficiency in medical imaging.
  arXiv  Detail & Related papers  (2024-11-19T16:20:27Z)
• It Takes Two: Accurate Gait Recognition in the Wild via Cross-granularity Alignment [72.75844404617959]
  This paper proposes a novel cross-granularity alignment gait recognition method, named XGait. To achieve this goal, the XGait first contains two branches of backbone encoders to map the silhouette sequences and the parsing sequences into two latent spaces. Comprehensive experiments on two large-scale gait datasets show XGait with the Rank-1 accuracy of 80.5% on Gait3D and 88.3% CCPG.
  arXiv  Detail & Related papers  (2024-11-16T08:54:27Z)
• Multi-modal Spatial Clustering for Spatial Transcriptomics Utilizing High-resolution Histology Images [1.3124513975412255]
  spatial transcriptomics (ST) enables transcriptome-wide gene expression profiling while preserving spatial context. Current spatial clustering methods fail to fully integrate high-resolution histology image features with gene expression data. We propose a novel contrastive learning-based deep learning approach that integrates gene expression data with histology image features.
  arXiv  Detail & Related papers  (2024-10-31T00:32:24Z)
• SSPA: Split-and-Synthesize Prompting with Gated Alignments for Multi-Label Image Recognition [71.90536979421093]
  We propose a Split-and-Synthesize Prompting with Gated Alignments (SSPA) framework to amplify the potential of Vision-Language Models (VLMs) We develop an in-context learning approach to associate the inherent knowledge from LLMs. Then we propose a novel Split-and-Synthesize Prompting (SSP) strategy to first model the generic knowledge and downstream label semantics individually.
  arXiv  Detail & Related papers  (2024-07-30T15:58:25Z)
• Perspective+ Unet: Enhancing Segmentation with Bi-Path Fusion and Efficient Non-Local Attention for Superior Receptive Fields [19.71033340093199]
  We propose a novel architecture, Perspective+ Unet, to overcome limitations in medical image segmentation. The framework incorporates an efficient non-local transformer block, named ENLTB, which utilizes kernel function approximation for effective long-range dependency capture. Experimental results on the ACDC and datasets demonstrate the effectiveness of our proposed Perspective+ Unet.
  arXiv  Detail & Related papers  (2024-06-20T07:17:39Z)
• GuideGen: A Text-Guided Framework for Full-torso Anatomy and CT Volume Generation [1.138481191622247]
  GuideGen is a controllable framework that generates anatomical masks and corresponding CT volumes for the entire torso-from chest to pelvis-based on free-form text prompts. Our approach includes three core components: a text-conditional semantic synthesizer for creating realistic full-torso anatomies; a contrast-aware autoencoder for detailed, high-fidelity feature extraction across varying contrast levels; and a latent feature generator that ensures alignment between CT images, anatomical semantics and input prompts.
  arXiv  Detail & Related papers  (2024-03-12T02:09:39Z)
• PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis [56.63327669853693]
  We propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also address the preservation of scale information, a powerful tool in aiding image understanding. The proposed unified SSL framework surpasses its self-supervised counterparts on various tasks.
  arXiv  Detail & Related papers  (2023-01-02T17:47:27Z)
• SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis [63.845552349914186]
  We propose a style-guided instance-adaptive normalization (SIAN) to synthesize realistic color distributions and textures for different organs. The four phases work together and are integrated into a generative network to embed image semantics, style, and instance-level boundaries.
  arXiv  Detail & Related papers  (2022-09-02T16:45:46Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• Contextual Information Enhanced Convolutional Neural Networks for Retinal Vessel Segmentation in Color Fundus Images [0.0]
  An automatic retinal vessel segmentation system can effectively facilitate clinical diagnosis and ophthalmological research. A deep learning based method has been proposed and several customized modules have been integrated into the well-known encoder-decoder architecture U-net. As a result, the proposed method outperforms the work of predecessors and achieves state-of-the-art performance in Sensitivity/Recall, F1-score and MCC.
  arXiv  Detail & Related papers  (2021-03-25T06:10:47Z)
• PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection [57.49788100647103]
  LiDAR-based 3D object detection is an important task for autonomous driving. Current approaches suffer from sparse and partial point clouds of distant and occluded objects. In this paper, we propose a novel two-stage approach, namely PC-RGNN, dealing with such challenges by two specific solutions.
  arXiv  Detail & Related papers  (2020-12-18T18:06:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.