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Register a new userGasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathology Image Classification
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Added by
Haoyuan Chen
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Existing deep learning methods for diagnosis of gastric cancer commonly use convolutional neural network. Recently, the Visual Transformer has attracted great attention because of its performance and efficiency, but its applications are mostly in the field of computer vision. In this paper, a multi-scale visual transformer model, referred to as GasHis-Transformer, is proposed for Gastric Histopathological Image Classification (GHIC), which enables the automatic classification of microscopic gastric images into abnormal and normal cases. The GasHis-Transformer model consists of two key modules: A global information module and a local information module to extract histopathological features effectively. In our experiments, a public hematoxylin and eosin (H&E) stained gastric histopathological dataset with 280 abnormal and normal images are divided into training, validation and test sets by a ratio of 1 : 1 : 2. The GasHis-Transformer model is applied to estimate precision, recall, F1-score and accuracy on the test set of gastric histopathological dataset as 98.0%, 100.0%, 96.0% and 98.0%, respectively. Furthermore, a critical study is conducted to evaluate the robustness of GasHis-Transformer, where ten different noises including four adversarial attack and six conventional image noises are added. In addition, a clinically meaningful study is executed to test the gastrointestinal cancer identification performance of GasHis-Transformer with 620 abnormal images and achieves 96.8% accuracy. Finally, a comparative study is performed to test the generalizability with both H&E and immunohistochemical stained images on a lymphoma image dataset and a breast cancer dataset, producing comparable F1-scores (85.6% and 82.8%) and accuracies (83.9% and 89.4%), respectively. In conclusion, GasHisTransformer demonstrates high classification performance and shows its significant potential in the GHIC task.
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GasHisSDB is a New Gastric Histopathology Subsize Image Database with a total of 245196 images. GasHisSDB is divided into 160*160 pixels sub-database, 120*120 pixels sub-database and 80*80 pixels sub-database. GasHisSDB is made to realize the function of valuating image classification. In order to prove that the methods of different periods in the field of image classification have discrepancies on GasHisSDB, we select a variety of classifiers for evaluation. Seven classical machine learning classifiers, three CNN classifiers and a novel transformer-based classifier are selected for testing on image classification tasks. GasHisSDB is available at the URL:https://github.com/NEUhwm/GasHisSDB.git.
The recently developed vision transformer (ViT) has achieved promising results on image classification compared to convolutional neural networks. Inspired by this, in this paper, we study how to learn multi-scale feature representations in transformer models for image classification. To this end, we propose a dual-branch transformer to combine image patches (i.e., tokens in a transformer) of different sizes to produce stronger image features. Our approach processes small-patch and large-patch tokens with two separate branches of different computational complexity and these tokens are then fused purely by attention multiple times to complement each other. Furthermore, to reduce computation, we develop a simple yet effective token fusion module based on cross attention, which uses a single token for each branch as a query to exchange information with other branches. Our proposed cross-attention only requires linear time for both computational and memory complexity instead of quadratic time otherwise. Extensive experiments demonstrate that our approach performs better than or on par with several concurrent works on vision transformer, in addition to efficient CNN models. For example, on the ImageNet1K dataset, with some architectural changes, our approach outperforms the recent DeiT by a large margin of 2% with a small to moderate increase in FLOPs and model parameters. Our source codes and models are available at url{https://github.com/IBM/CrossViT}.
In the Gastric Histopathology Image Classification (GHIC) tasks, which are usually weakly supervised learning missions, there is inevitably redundant information in the images. Therefore, designing networks that can focus on effective distinguishing features has become a popular research topic. In this paper, to accomplish the tasks of GHIC superiorly and to assist pathologists in clinical diagnosis, an intelligent Hierarchical Conditional Random Field based Attention Mechanism (HCRF-AM) model is proposed. The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classification (IC) module. In the AM module, an HCRF model is built to extract attention regions. In the IC module, a Convolutional Neural Network (CNN) model is trained with the attention regions selected and then an algorithm called Classification Probability-based Ensemble Learning is applied to obtain the image-level results from patch-level output of the CNN. In the experiment, a classification specificity of 96.67% is achieved on a gastric histopathology dataset with 700 images. Our HCRF-AM model demonstrates high classification performance and shows its effectiveness and future potential in the GHIC field.
Image quality assessment (IQA) is an important research topic for understanding and improving visual experience. The current state-of-the-art IQA methods are based on convolutional neural networks (CNNs). The performance of CNN-based models is often compromised by the fixed shape constraint in batch training. To accommodate this, the input images are usually resized and cropped to a fixed shape, causing image quality degradation. To address this, we design a multi-scale image quality Transformer (MUSIQ) to process native resolution images with varying sizes and aspect ratios. With a multi-scale image representation, our proposed method can capture image quality at different granularities. Furthermore, a novel hash-based 2D spatial embedding and a scale embedding is proposed to support the positional embedding in the multi-scale representation. Experimental results verify that our method can achieve state-of-the-art performance on multiple large scale IQA datasets such as PaQ-2-PiQ, SPAQ and KonIQ-10k.
Many animals and humans process the visual field with a varying spatial resolution (foveated vision) and use peripheral processing to make eye movements and point the fovea to acquire high-resolution information about objects of interest. This architecture results in computationally efficient rapid scene exploration. Recent progress in vision Transformers has brought about new alternatives to the traditionally convolution-reliant computer vision systems. However, these models do not explicitly model the foveated properties of the visual system nor the interaction between eye movements and the classification task. We propose foveated Transformer (FoveaTer) model, which uses pooling regions and saccadic movements to perform object classification tasks using a vision Transformer architecture. Our proposed model pools the image features using squared pooling regions, an approximation to the biologically-inspired foveated architecture, and uses the pooled features as an input to a Transformer Network. It decides on the following fixation location based on the attention assigned by the Transformer to various locations from previous and present fixations. The model uses a confidence threshold to stop scene exploration, allowing to dynamically allocate more fixation/computational resources to more challenging images. We construct an ensemble model using our proposed model and unfoveated model, achieving an accuracy 1.36% below the unfoveated model with 22% computational savings. Finally, we demonstrate our models robustness against adversarial attacks, where it outperforms the unfoveated model.
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