PathoDuet: Foundation Models for Pathological Slide Analysis of H&E and IHC Stains

• URL: http://arxiv.org/abs/2312.09894v2
• Date: Mon, 5 Aug 2024 19:21:16 GMT
• Title: PathoDuet: Foundation Models for Pathological Slide Analysis of H&E and IHC Stains
• Authors: Shengyi Hua, Fang Yan, Tianle Shen, Lei Ma, Xiaofan Zhang,
• Abstract summary: We present PathoDuet, a series of pretrained models on histopathology images, and a new self-supervised learning framework in histochemistry. The framework is featured by a newly-introduced pretext token and later task raisers to explicitly utilize certain relations between images. Two pretext tasks, cross-scale positioning and cross-stain transferring, are designed to pretrain the model on Hematoxylin and Eosin images.
• Score: 5.422494000842841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large amounts of digitized histopathological data display a promising future for developing pathological foundation models via self-supervised learning methods. Foundation models pretrained with these methods serve as a good basis for downstream tasks. However, the gap between natural and histopathological images hinders the direct application of existing methods. In this work, we present PathoDuet, a series of pretrained models on histopathological images, and a new self-supervised learning framework in histopathology. The framework is featured by a newly-introduced pretext token and later task raisers to explicitly utilize certain relations between images, like multiple magnifications and multiple stains. Based on this, two pretext tasks, cross-scale positioning and cross-stain transferring, are designed to pretrain the model on Hematoxylin and Eosin (H&E) images and transfer the model to immunohistochemistry (IHC) images, respectively. To validate the efficacy of our models, we evaluate the performance over a wide variety of downstream tasks, including patch-level colorectal cancer subtyping and whole slide image (WSI)-level classification in H&E field, together with expression level prediction of IHC marker, tumor identification and slide-level qualitative analysis in IHC field. The experimental results show the superiority of our models over most tasks and the efficacy of proposed pretext tasks. The codes and models are available at https://github.com/openmedlab/PathoDuet.

Related papers

• Going Beyond H&E and Oncology: How Do Histopathology Foundation Models Perform for Multi-stain IHC and Immunology? [0.0]
  This study evaluates the capabilities of state-of-the-art histopathology foundation models on autoimmune ImmunoBench datasets. We compare 13 feature extractor models, including ImageNet-pretrained networks, and histopathology foundation models trained on both public and proprietary data.
  arXiv  Detail & Related papers  (2024-10-28T21:48:39Z)
• PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation [51.509573838103854]
  We propose a semi-supervised learning framework, termed Progressive Mean Teachers (PMT), for medical image segmentation. Our PMT generates high-fidelity pseudo labels by learning robust and diverse features in the training process. Experimental results on two datasets with different modalities, i.e., CT and MRI, demonstrate that our method outperforms the state-of-the-art medical image segmentation approaches.
  arXiv  Detail & Related papers  (2024-09-08T15:02:25Z)
• Classification of lung cancer subtypes on CT images with synthetic pathological priors [41.75054301525535]
  Cross-scale associations exist in the image patterns between the same case's CT images and its pathological images. We propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on CT images.
  arXiv  Detail & Related papers  (2023-08-09T02:04:05Z)
• Towards a Visual-Language Foundation Model for Computational Pathology [5.72536252929528]
  We introduce CONtrastive learning from Captions for Histopathology (CONCH) CONCH is a visual-language foundation model developed using diverse sources of histopathology images, biomedical text, and task-agnostic pretraining. It is evaluated on a suite of 13 diverse benchmarks, achieving state-of-the-art performance on histology image classification, segmentation, captioning, text-to-image and image-to-text retrieval.
  arXiv  Detail & Related papers  (2023-07-24T16:13:43Z)
• The Whole Pathological Slide Classification via Weakly Supervised Learning [7.313528558452559]
  We introduce two pathological priors: nuclear disease of cells and spatial correlation of pathological tiles. We propose a data augmentation method that utilizes stain separation during extractor training. We then describe the spatial relationships between the tiles using an adjacency matrix. By integrating these two views, we designed a multi-instance framework for analyzing H&E-stained tissue images.
  arXiv  Detail & Related papers  (2023-07-12T16:14:23Z)
• Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification [15.354256205808273]
  Whole Slide Images (WSIs) or histopathology images are used in digital pathology. WSIs pose great challenges to deep learning models for clinical diagnosis.
  arXiv  Detail & Related papers  (2023-02-20T08:42:06Z)
• Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification [52.94051907952536]
  We propose embedding metric learning into the first stage of the two-stage framework specially to help the feature extractor learn to extract more discriminative feature representations. Experiments mainly on three medical image datasets show that the proposed approach consistently outperforms existing onestage and two-stage approaches.
  arXiv  Detail & Related papers  (2022-07-14T14:57:01Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples [52.549928980694695]
  In situ hybridization (ISH) gene expression pattern images from the same developmental stage are compared. labeling training data with precise stages is very time-consuming even for biologists. We propose a deep two-step low-shot learning framework to accurately classify ISH images using limited training images.
  arXiv  Detail & Related papers  (2020-10-20T06:06:06Z)
• Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks [68.96150598294072]
  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights. It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness. In recent years, there has been a significant effort to automate the diagnosis using deep learning. This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
  arXiv  Detail & Related papers  (2020-04-03T14:07:41Z)

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.