Beyond H&E: Unlocking Pathological Insights with Polarization via Self-supervised Learning

• URL: http://arxiv.org/abs/2503.05933v1
• Date: Wed, 05 Mar 2025 05:00:19 GMT
• Title: Beyond H&E: Unlocking Pathological Insights with Polarization via Self-supervised Learning
• Authors: Yao Du, Jiaxin Zhuang, Xiaoyu Zheng, Jing Cong, Limei Guo, Chao He, Lin Luo, Xiaomeng Li,
• Abstract summary: Histopathology is fundamental to digital pathology, with hematoxylin and eosin staining as the gold standard for diagnostic and prognostic assessments.<n>While H&E imaging effectively highlights cellular and tissue structures, it lacks sensitivity to birefringence and tissue anisotropy.<n>We propose PolarHE, a dual modality fusion framework that integrates H&E with polarization imaging.
• Score: 9.290835226997961
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Histopathology image analysis is fundamental to digital pathology, with hematoxylin and eosin (H&E) staining as the gold standard for diagnostic and prognostic assessments. While H&E imaging effectively highlights cellular and tissue structures, it lacks sensitivity to birefringence and tissue anisotropy, which are crucial for assessing collagen organization, fiber alignment, and microstructural alterations--key indicators of tumor progression, fibrosis, and other pathological conditions. To bridge this gap, we propose PolarHE, a dual modality fusion framework that integrates H&E with polarization imaging, leveraging the polarization ability to enhance tissue characterization. Our approach employs a feature decomposition strategy to disentangle common and modality specific features, ensuring effective multimodal representation learning. Through comprehensive validation, our approach significantly outperforms previous methods, achieving an accuracy of 86.70% on the Chaoyang dataset and 89.06% on the MHIST dataset. Moreover, polarization property visualization reveals distinct optical signatures of pathological tissues, highlighting its diagnostic potential. t-SNE visualizations further confirm our model effectively captures both shared and unique modality features, reinforcing the complementary nature of polarization imaging. These results demonstrate that polarization imaging is a powerful and underutilized modality in computational pathology, enriching feature representation and improving diagnostic accuracy. PolarHE establishes a promising direction for multimodal learning, paving the way for more interpretable and generalizable pathology models. Our code will be released after paper acceptance.

Related papers

• An Explainable Transformer Model for Alzheimer's Disease Detection Using Retinal Imaging [5.3785187022022845]
  Alzheimer's disease (AD) is a neurodegenerative disorder that affects millions worldwide.<n>In this study, we propose Retformer, a novel transformer-based architecture for detecting AD using retinal imaging modalities.
  arXiv  Detail & Related papers  (2025-07-06T06:40:42Z)
• ADNP-15: An Open-Source Histopathological Dataset for Neuritic Plaque Segmentation in Human Brain Whole Slide Images with Frequency Domain Image Enhancement for Stain Normalization [7.9997900784270035]
  Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by amyloid-beta plaques and tau neurofibrillary tangles.<n>The identification and segmentation of these lesions are crucial for understanding AD progression.<n>Deep learning has emerged as a powerful tool for pathology image segmentation.
  arXiv  Detail & Related papers  (2025-05-08T08:25:44Z)
• Towards Accurate and Interpretable Neuroblastoma Diagnosis via Contrastive Multi-scale Pathological Image Analysis [16.268045905735818]
  CMSwinKAN is a contrastive-learning-based multi-scale feature fusion model tailored for pathological image classification. We introduce a soft voting mechanism guided by clinical insights to seamlessly bridge patch-level predictions to whole slide image-level classifications. Results demonstrate that CMSwinKAN performs better than existing state-of-the-art pathology-specific models pre-trained on large datasets.
  arXiv  Detail & Related papers  (2025-04-18T15:39:46Z)
• Enhancing Fundus Image-based Glaucoma Screening via Dynamic Global-Local Feature Integration [26.715346685730484]
  We propose a self-adaptive attention window that autonomously determines optimal boundaries for enhanced feature extraction. We also introduce a multi-head attention mechanism to effectively fuse global and local features via feature linear readout. Experimental results demonstrate that our method achieves superior accuracy and robustness in glaucoma classification.
  arXiv  Detail & Related papers  (2025-04-01T05:28:14Z)
• Retinal Fundus Multi-Disease Image Classification using Hybrid CNN-Transformer-Ensemble Architectures [0.3277163122167434]
  Our research is motivated by the urgent global issue of a large population affected by retinal diseases. Our primary objective is to develop a comprehensive diagnostic system capable of accurately predicting retinal diseases.
  arXiv  Detail & Related papers  (2025-03-27T12:55:07Z)
• DCAT: Dual Cross-Attention Fusion for Disease Classification in Radiological Images with Uncertainty Estimation [0.0]
  This paper proposes a novel dual cross-attention fusion model for medical image analysis. It addresses key challenges in feature integration and interpretability. The proposed model achieved AUC of 99.75%, 100%, 99.93% and 98.69% and AUPR of 99.81%, 100%, 99.97%, and 96.36% on Covid-19, Tuberculosis, Pneumonia Chest X-ray images and Retinal OCT images respectively.
  arXiv  Detail & Related papers  (2025-03-14T20:28:20Z)
• DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation [17.396365010722423]
  Retinal vascular morphology is crucial for diagnosing diseases such as diabetes, glaucoma, and hypertension.<n>Traditional segmentation methods assume that training and testing data share similar distributions, which can lead to poor performance on unseen domains.<n>This paper presents a novel approach, DGSSA, for retinal vessel image segmentation that enhances model generalization by combining structural and style augmentation strategies.
  arXiv  Detail & Related papers  (2025-01-07T01:47:57Z)
• Beyond the Eye: A Relational Model for Early Dementia Detection Using Retinal OCTA Images [42.75763279888966]
  We present a novel PolarNet+ that uses retinal optical coherence tomography angiography ( OCTA) to discriminate early-onset Alzheimer's disease (AD) and mild cognitive impairment (MCI) subjects from controls. Our method first maps OCTA images from Cartesian coordinates to polar coordinates, allowing approximate sub-region calculation. We then introduce a multi-view module to serialize and analyze the images along three dimensions for comprehensive, clinically useful information extraction.
  arXiv  Detail & Related papers  (2024-08-09T15:10:34Z)
• A Polarization and Radiomics Feature Fusion Network for the Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma [10.403042621991611]
  Classifying carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) is a critical step in treatment selection and prognosis evaluation. In this study, we introduce a novel polarization and radiomics feature fusion network, which combines polarization features with radiomics features to classify HCC and ICC.
  arXiv  Detail & Related papers  (2023-12-27T15:16:04Z)
• Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection in OCTA Images [53.235117594102675]
  Optical Coherence Tomography Angiography is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature. We propose a novel deep-learning framework called Polar-Net to provide interpretable results and leverage clinical prior knowledge. We show that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD.
  arXiv  Detail & Related papers  (2023-11-10T11:49:49Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Segmentation of Anatomical Layers and Artifacts in Intravascular Polarization Sensitive Optical Coherence Tomography Using Attending Physician and Boundary Cardinality Lost Terms [4.93836246080317]
  Intravascular ultrasound and optical coherence tomography are widely available for characterizing coronary stenoses. We propose a convolutional neural network model and optimize its performance using a new multi-term loss function. Our model segments two classes of major artifacts and detects the anatomical layers within the thickened vessel wall regions.
  arXiv  Detail & Related papers  (2021-05-11T15:52:31Z)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• Modeling and Enhancing Low-quality Retinal Fundus Images [167.02325845822276]
  Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. We propose a clinically oriented fundus enhancement network (cofe-Net) to suppress global degradation factors. Experiments on both synthetic and real images demonstrate that our algorithm effectively corrects low-quality fundus images without losing retinal details.
  arXiv  Detail & Related papers  (2020-05-12T08:01:16Z)

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.