Global Contrast Masked Autoencoders Are Powerful Pathological
Representation Learners
- URL: http://arxiv.org/abs/2205.09048v4
- Date: Thu, 16 Nov 2023 03:16:03 GMT
- Title: Global Contrast Masked Autoencoders Are Powerful Pathological
Representation Learners
- Authors: Hao Quan, Xingyu Li, Weixing Chen, Qun Bai, Mingchen Zou, Ruijie Yang,
Tingting Zheng, Ruiqun Qi, Xinghua Gao, Xiaoyu Cui
- Abstract summary: We propose a self-supervised learning model, the global contrast-masked autoencoder (GCMAE), which can train the encoder to have the ability to represent local-global features of pathological images.
The ability of the GCMAE to learn migratable representations was demonstrated through extensive experiments using a total of three different disease-specific hematoxylin and eosin (HE)-stained pathology datasets.
- Score: 11.162001837248166
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Based on digital pathology slice scanning technology, artificial intelligence
algorithms represented by deep learning have achieved remarkable results in the
field of computational pathology. Compared to other medical images, pathology
images are more difficult to annotate, and thus, there is an extreme lack of
available datasets for conducting supervised learning to train robust deep
learning models. In this paper, we propose a self-supervised learning (SSL)
model, the global contrast-masked autoencoder (GCMAE), which can train the
encoder to have the ability to represent local-global features of pathological
images, also significantly improve the performance of transfer learning across
data sets. In this study, the ability of the GCMAE to learn migratable
representations was demonstrated through extensive experiments using a total of
three different disease-specific hematoxylin and eosin (HE)-stained pathology
datasets: Camelyon16, NCTCRC and BreakHis. In addition, this study designed an
effective automated pathology diagnosis process based on the GCMAE for clinical
applications. The source code of this paper is publicly available at
https://github.com/StarUniversus/gcmae.
Related papers
- Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspective [32.93871326428446]
Recent advances in artificial intelligence (AI) are revolutionizing medical imaging and computational pathology.
A constant challenge in the analysis of digital Whole Slide Images (WSIs) is the problem of aggregating tens of thousands of tile-level image embeddings to a slide-level representation.
This study conducts a benchmarking analysis of ten slide-level aggregation techniques across nine clinically relevant tasks.
arXiv Detail & Related papers (2024-07-10T17:00:57Z) - Feature Representation Learning for Robust Retinal Disease Detection
from Optical Coherence Tomography Images [0.0]
Ophthalmic images may contain identical-looking pathologies that can cause failure in automated techniques to distinguish different retinal degenerative diseases.
In this work, we propose a robust disease detection architecture with three learning heads.
Our experimental results on two publicly available OCT datasets illustrate that the proposed model outperforms existing state-of-the-art models in terms of accuracy, interpretability, and robustness for out-of-distribution retinal disease detection.
arXiv Detail & Related papers (2022-06-24T07:59:36Z) - Deepfake histological images for enhancing digital pathology [0.40631409309544836]
We develop a generative adversarial network model that synthesizes pathology images constrained by class labels.
We investigate the ability of this framework in synthesizing realistic prostate and colon tissue images.
We extend the approach to significantly more complex images from colon biopsies and show that the complex microenvironment in such tissues can also be reproduced.
arXiv Detail & Related papers (2022-06-16T17:11:08Z) - Self-Supervised Vision Transformers Learn Visual Concepts in
Histopathology [5.164102666113966]
We conduct a search for good representations in pathology by training a variety of self-supervised models with validation on a variety of weakly-supervised and patch-level tasks.
Our key finding is in discovering that Vision Transformers using DINO-based knowledge distillation are able to learn data-efficient and interpretable features in histology images.
arXiv Detail & Related papers (2022-03-01T16:14:41Z) - Incremental Cross-view Mutual Distillation for Self-supervised Medical
CT Synthesis [88.39466012709205]
This paper builds a novel medical slice to increase the between-slice resolution.
Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy.
Our method outperforms state-of-the-art algorithms by clear margins.
arXiv Detail & Related papers (2021-12-20T03:38:37Z) - Medulloblastoma Tumor Classification using Deep Transfer Learning with
Multi-Scale EfficientNets [63.62764375279861]
We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions.
Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
arXiv Detail & Related papers (2021-09-10T13:07:11Z) - Self-Supervised Representation Learning using Visual Field Expansion on
Digital Pathology [7.568373895297608]
A key challenge in the analysis of such images is their size, which can run into the gigapixels.
We propose a novel generative framework that can learn powerful representations for such tiles by learning to plausibly expand their visual field.
Our model learns to generate different tissue types with fine details, while simultaneously learning powerful representations that can be used for different clinical endpoints.
arXiv Detail & Related papers (2021-09-07T19:20:01Z) - In-Line Image Transformations for Imbalanced, Multiclass Computer Vision
Classification of Lung Chest X-Rays [91.3755431537592]
This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data.
Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states.
This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
arXiv Detail & Related papers (2021-04-06T02:01:43Z) - 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) - Few-shot Medical Image Segmentation using a Global Correlation Network
with Discriminative Embedding [60.89561661441736]
We propose a novel method for few-shot medical image segmentation.
We construct our few-shot image segmentor using a deep convolutional network trained episodically.
We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
arXiv Detail & Related papers (2020-12-10T04:01:07Z) - Fader Networks for domain adaptation on fMRI: ABIDE-II study [68.5481471934606]
We use 3D convolutional autoencoders to build the domain irrelevant latent space image representation and demonstrate this method to outperform existing approaches on ABIDE data.
arXiv Detail & Related papers (2020-10-14T16:50:50Z)
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.