MLLM4PUE: Toward Universal Embeddings in Digital Pathology through Multimodal LLMs

• URL: http://arxiv.org/abs/2502.07221v2
• Date: Sun, 16 Mar 2025 20:05:51 GMT
• Title: MLLM4PUE: Toward Universal Embeddings in Digital Pathology through Multimodal LLMs
• Authors: Qifeng Zhou, Thao M. Dang, Wenliang Zhong, Yuzhi Guo, Hehuan Ma, Saiyang Na, Haiqing Li, Junzhou Huang,
• Abstract summary: We highlight the need for universal multimodal embeddings that can support multiple downstream tasks.<n>Previous approaches involve fine-tuning CLIP-based models, which handle images and texts separately.<n>We propose MLLM4PUE, a novel framework that leverages MLLMs to generate embeddings for various pathology downstream tasks.
• Score: 34.092892344250025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pathology plays a critical role in diagnosing a wide range of diseases, yet existing approaches often rely heavily on task-specific models trained on extensive, well-labeled datasets. These methods face sustainability challenges due to the diversity of pathologies and the labor-intensive nature of data collection. To address these limitations, we highlight the need for universal multimodal embeddings that can support multiple downstream tasks. Previous approaches involve fine-tuning CLIP-based models, which handle images and texts separately, limiting their ability to capture complex multimodal relationships. Additionally, these models are evaluated across diverse datasets without a unified benchmark. In this paper, we explore the possibility of applying Multimodal Large Language Models (MLLMs) to generate pathology universal embeddings to address these challenges. Our contributions can be summarized in the following aspects: 1) We propose MLLM4PUE, a novel framework that leverages MLLMs to generate embeddings for various pathology downstream tasks. 2) We further introduce the Pathology Multimodal Embedding Benchmark (PMEB), a comprehensive benchmark designed to assess the quality of pathology multimodal embeddings, which comprises 16 original tasks drawn from 15 datasets. 3) Extensive experimental results demonstrate the superiority of MLLM4PUE, illustrating MLLM-based models can effectively support a wide range of downstream tasks and unify the research direction for foundation models in pathology.

Related papers

• Robust Multimodal Learning for Ophthalmic Disease Grading via Disentangled Representation [30.697291934309206]
  multimodal data is rare in real-world applications due to a lack of medical equipment and concerns about data privacy. Traditional deep learning methods typically address these issues by learning representations in latent space. Authors propose the Essence-Point and Disentangle Representation Learning (EDRL) strategy, which integrates a self-distillation mechanism into an end-to-end framework.
  arXiv  Detail & Related papers  (2025-03-07T10:58:38Z)
• A Survey on Mechanistic Interpretability for Multi-Modal Foundation Models [74.48084001058672]
  The rise of foundation models has transformed machine learning research. multimodal foundation models (MMFMs) pose unique interpretability challenges beyond unimodal frameworks. This survey explores two key aspects: (1) the adaptation of LLM interpretability methods to multimodal models and (2) understanding the mechanistic differences between unimodal language models and crossmodal systems.
  arXiv  Detail & Related papers  (2025-02-22T20:55:26Z)
• FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data [56.08867996209236]
  Fine-tuning Multimodal Large Language Models (MLLMs) with Federated Learning (FL) allows for expanding the training data scope by including private data sources. We introduce a benchmark to evaluate the performance of federated fine-tuning of MLLMs across various multimodal heterogeneous scenarios. We develop a general FedMLLM framework that integrates classic FL methods alongside two modality-agnostic strategies.
  arXiv  Detail & Related papers  (2024-11-22T04:09:23Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework [58.362064122489166]
  This paper introduces the Cross-modal Few-Shot Learning task, which aims to recognize instances across multiple modalities while relying on scarce labeled data. We propose a Generative Transfer Learning framework by simulating how humans abstract and generalize concepts. We show that the GTL achieves state-of-the-art performance across seven multi-modal datasets across RGB-Sketch, RGB-Infrared, and RGB-Depth.
  arXiv  Detail & Related papers  (2024-10-14T16:09:38Z)
• MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct [148.39859547619156]
  We propose MMEvol, a novel multimodal instruction data evolution framework.<n>MMEvol iteratively improves data quality through a refined combination of fine-grained perception, cognitive reasoning, and interaction evolution.<n>Our approach reaches state-of-the-art (SOTA) performance in nine tasks using significantly less data compared to state-of-the-art models.
  arXiv  Detail & Related papers  (2024-09-09T17:44:00Z)
• PathInsight: Instruction Tuning of Multimodal Datasets and Models for Intelligence Assisted Diagnosis in Histopathology [7.87900104748629]
  We have meticulously compiled a dataset of approximately 45,000 cases, covering over 6 different tasks. We have fine-tuned multimodal large models, specifically LLaVA, Qwen-VL, InternLM, with this dataset to enhance instruction-based performance.
  arXiv  Detail & Related papers  (2024-08-13T17:05:06Z)
• A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks [74.52259252807191]
  Multimodal Large Language Models (MLLMs) address the complexities of real-world applications far beyond the capabilities of single-modality systems. This paper systematically sorts out the applications of MLLM in multimodal tasks such as natural language, vision, and audio.
  arXiv  Detail & Related papers  (2024-08-02T15:14:53Z)
• A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model [13.96693863133633]
  We curated the largest multimodal dataset consisting of H&E diagnostic whole slide images and their associated pathology reports and RNA-Seq data. We propose a novel whole-slide pretraining paradigm which injects multimodal knowledge at the whole-slide context into the pathology FM. The proposed paradigm revolutionizes the workflow of pretraining for CPath, which enables the pathology FM to acquire the whole-slide context.
  arXiv  Detail & Related papers  (2024-07-22T04:09:27Z)
• FlexCare: Leveraging Cross-Task Synergy for Flexible Multimodal Healthcare Prediction [34.732561455987145]
  We propose a unified healthcare prediction model, also named by textbfFlexCare, to flexibly accommodate incomplete multimodal inputs. A task-agnostic multimodal information extraction module is presented to capture decorrelated representations of diverse intra- and inter-modality patterns. Experimental results on multiple tasks from MIMIC-IV/MIMIC-CXR/MIMIC-NOTE datasets demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2024-06-17T12:03:10Z)
• PLUTO: Pathology-Universal Transformer [4.920983796208486]
  We propose PathoLogy Universal TransfOrmer (PLUTO): a light-weight pathology FM that is pre-trained on a diverse dataset of 195 million image tiles. We design task-specific adaptation heads that utilize PLUTO's output embeddings for tasks which span pathology scales. We find that PLUTO matches or outperforms existing task-specific baselines and pathology-specific foundation models.
  arXiv  Detail & Related papers  (2024-05-13T16:40:17Z)
• MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks [56.60050181186531]
  We introduce MM-BigBench, which incorporates a diverse range of metrics to offer an extensive evaluation of the performance of various models and instructions. Our paper evaluates a total of 20 language models (14 MLLMs) on 14 multimodal datasets spanning 6 tasks, with 10 instructions for each task, and derives novel insights.
  arXiv  Detail & Related papers  (2023-10-13T11:57:04Z)
• On the Performance of Multimodal Language Models [4.677125897916577]
  This study conducts a comparative analysis of different multimodal instruction tuning approaches. We reveal key insights for guiding architectural choices when incorporating multimodal capabilities into large language models.
  arXiv  Detail & Related papers  (2023-10-04T23:33:36Z)
• A Survey on Multimodal Large Language Models [71.63375558033364]
  Multimodal Large Language Model (MLLM) represented by GPT-4V has been a new rising research hotspot.<n>This paper aims to trace and summarize the recent progress of MLLMs.
  arXiv  Detail & Related papers  (2023-06-23T15:21:52Z)

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.