PathM3: A Multimodal Multi-Task Multiple Instance Learning Framework for Whole Slide Image Classification and Captioning

• URL: http://arxiv.org/abs/2403.08967v2
• Date: Tue, 23 Jul 2024 20:14:17 GMT
• Title: PathM3: A Multimodal Multi-Task Multiple Instance Learning Framework for Whole Slide Image Classification and Captioning
• Authors: Qifeng Zhou, Wenliang Zhong, Yuzhi Guo, Michael Xiao, Hehuan Ma, Junzhou Huang,
• Abstract summary: We present PathM3, a multi-task, multiple instance learning framework for WSI classification and captioning. Our method overcomes data scarcity in WSI-level captions by leveraging limited WSI diagnostic caption data.
• Score: 35.24716774767677
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the field of computational histopathology, both whole slide images (WSIs) and diagnostic captions provide valuable insights for making diagnostic decisions. However, aligning WSIs with diagnostic captions presents a significant challenge. This difficulty arises from two main factors: 1) Gigapixel WSIs are unsuitable for direct input into deep learning models, and the redundancy and correlation among the patches demand more attention; and 2) Authentic WSI diagnostic captions are extremely limited, making it difficult to train an effective model. To overcome these obstacles, we present PathM3, a multimodal, multi-task, multiple instance learning (MIL) framework for WSI classification and captioning. PathM3 adapts a query-based transformer to effectively align WSIs with diagnostic captions. Given that histopathology visual patterns are redundantly distributed across WSIs, we aggregate each patch feature with MIL method that considers the correlations among instances. Furthermore, our PathM3 overcomes data scarcity in WSI-level captions by leveraging limited WSI diagnostic caption data in the manner of multi-task joint learning. Extensive experiments with improved classification accuracy and caption generation demonstrate the effectiveness of our method on both WSI classification and captioning task.

Related papers

• ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classification [52.405499816861635]
  Multiple instance learning (MIL)-based framework has become the mainstream for processing the whole slide image (WSI) We propose a dual-scale vision-language multiple instance learning (ViLa-MIL) framework for whole slide image classification.
  arXiv  Detail & Related papers  (2025-02-12T13:28:46Z)
• 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)
• MSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt Tuning [11.717352903130411]
  Multiple instance learning (MIL) has become a standard paradigm for weakly supervised classification of whole slide images (WSI) The lack of training data and the presence of rare diseases present significant challenges for these methods. We propose a Multi-Scale and Context-focused Prompt Tuning (MSCPT) method for FSWC tasks.
  arXiv  Detail & Related papers  (2024-08-21T10:25:51Z)
• PathAlign: A vision-language model for whole slide images in histopathology [13.567674461880905]
  We develop a vision-language model based on the BLIP-2 framework using WSIs and curated text from pathology reports. This enables applications utilizing a shared image-text embedding space, such as text or image retrieval for finding cases of interest. We present pathologist evaluation of text generation and text retrieval using WSI embeddings, as well as results for WSI classification and workflow prioritization.
  arXiv  Detail & Related papers  (2024-06-27T23:43:36Z)
• A self-supervised framework for learning whole slide representations [52.774822784847565]
  We present Slide Pre-trained Transformers (SPT) for gigapixel-scale self-supervision of whole slide images. We benchmark SPT visual representations on five diagnostic tasks across three biomedical microscopy datasets.
  arXiv  Detail & Related papers  (2024-02-09T05:05:28Z)
• Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens [76.40196364163663]
  We propose a learning-based vision-language pre-training approach, such as CLIP. We show that our method can learn more comprehensive representations and capture meaningful cross-modal correspondence.
  arXiv  Detail & Related papers  (2023-03-27T00:58:39Z)
• A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Images [12.961686610789416]
  Self-supervised learning (SSL) has emerged as an alternative solution to reduce the annotation overheads in whole slide images. These SSL approaches are not designed for handling multi-resolution WSIs, which limits their performance in learning discriminative image features. We propose a Dual-branch SSL Framework for WSI tumour segmentation (DSF-WSI) that can effectively learn image features from multi-resolution WSIs.
  arXiv  Detail & Related papers  (2023-03-20T10:57:28Z)
• Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing [53.89917396428747]
  Self-supervised learning in vision-language processing exploits semantic alignment between imaging and text modalities. We explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model.
  arXiv  Detail & Related papers  (2023-01-11T16:35:33Z)
• Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomics [63.76637479503006]
  Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical. This paper proposes a hierarchical-based multimodal transformer framework that learns a hierarchical mapping between pathology images and corresponding genes. Our architecture requires fewer GPU resources compared with benchmark methods while maintaining better WSI representation ability.
  arXiv  Detail & Related papers  (2022-11-29T23:47:56Z)

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.