Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of DenseNet is introduced; second, the development of DenseNet is summarized and analyzed from five aspects: broaden DenseNet structure, lightweight DenseNet structure, dense unit, dense connection mode, and attention mechanism; finally, the application research of DenseNet in the field of medical image analysis is summarized from three aspects: pattern recognition, image segmentation, and object detection. The network structures of DenseNet are systematically summarized in this paper, which has certain positive significance for the research and development of DenseNet.

/pdf/dense-convolutional-network-and-its-application-in-medical-1kv271hd.pdf

Dense Convolutional Network and Its Application in Medical Image Analysis

/pdf/artificial-intelligence-for-breast-cancer-analysis-trends-3ukrzk2b.pdf

Artificial intelligence for breast cancer analysis: Trends &amp; directions

Breast cancer detection using an ensemble deep learning method

Breast cancer is regarded as the leading killer of women today. The early diagnosis and treatment of breast cancer is the key to improving the survival rate of patients. A method of breast cancer histopathological images recognition based on deep semantic features and gray level co-occurrence matrix (GLCM) features is proposed in this paper. Taking the pre-trained DenseNet201 as the basic model, part of the convolutional layer features of the last dense block are extracted as the deep semantic features, which are then fused with the three-channel GLCM features, and the support vector machine (SVM) is used for classification. For the BreaKHis dataset, we explore the classification problems of magnification specific binary (MSB) classification and magnification independent binary (MIB) classification, and compared the performance with the seven baseline models of AlexNet, VGG16, ResNet50, GoogLeNet, DenseNet201, SqueezeNet and Inception-ResNet-V2. The experimental results show that the method proposed in this paper performs better than the pre-trained baseline models in MSB and MIB classification problems. The highest image-level recognition accuracy of 40×, 100×, 200×, 400× is 96.75%, 95.21%, 96.57%, and 93.15%, respectively. And the highest patient-level recognition accuracy of the four magnifications is 96.33%, 95.26%, 96.09%, and 92.99%, respectively. The image-level and patient-level recognition accuracy for MIB classification is 95.56% and 95.54%, respectively. In addition, the recognition accuracy of the method in this paper is comparable to some state-of-the-art methods.

Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix

The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning‐based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field.

/pdf/a-survey-on-artificial-intelligence-in-histopathology-image-7y53n19b.pdf

A survey on artificial intelligence in histopathology image analysis

Computer-aided diagnosis (CAD) systems of breast cancer histopathological images automated classification can help reduce the manual observation workload of pathologists. In the classification of breast cancer histopathology images, due to the small number and high-resolution of the training samples, the patch-based image classification methods have become very necessary. However, adopting a patches-based classification method is very challenging, since the patch-level datasets extracted from whole slide images (WSIs) contain many mislabeled patches. Existing patch-based classification methods have paid little attention to addressing the mislabeled patches for improving the performance of classification. To solve this problem, we propose a novel approach, named DenseNet121-AnoGAN, for classifying breast histopathological images into benign and malignant classes. The proposed approach consists of two major parts: using an unsupervised anomaly detection with generative adversarial networks (AnoGAN) to screen mislabeled patches and using densely connected convolutional network (DenseNet) to extract multi-layered features of the discriminative patches. The performance of the proposed approach is evaluated on the publicly available BreaKHis dataset using 5-fold cross validation. The proposed DenseNet121-AnoGAN can be better suited to coarse-grained high-resolution images and achieved satisfactory classification performance in 40X and 100X images. The best accuracy of 99.13% and the best $F1_{score}$ of 99.38% have been obtained at the image level for the 40X magnification factor. We have also investigated the performance of AnoGAN on the other classification networks, including AlexNet, VGG16, VGG19, and ResNet50. Our experiments show that whether it is at the patient-level accuracy or at the image-level accuracy, the classification networks with AnoGAN have provided better performance than the classification networks without AnoGAN.

/pdf/classification-of-breast-cancer-histopathological-images-2xivi9px36.pdf

Classification of Breast Cancer Histopathological Images Using Discriminative Patches Screened by Generative Adversarial Networks

Generally, on the same data set, different deep learning classification models will achieve different performances. The existing weighted voting method can combine the results of models, which can improve the performance of classification. On the other side, its classification accuracy is affected by the accuracy of all models. In this paper, we proposed a dynamic weighted voting method. Our method dynamically selects models on different data sets, and integrates them according to their weights, thereby improving the classification accuracy. We evaluated the methods on three data sets of CIFAR10, CIFAR100 and Existing, which increased the accuracy about 0.65%, 0.91%, and 0.78% respectively compared with the existing weighted voting method.

https://iopscience.iop.org/article/10.1088/1742-6596/2185/1/012030/pdf

The Optimization of Multi-classifier Ensemble Method Based on Dynamic Weighted Voting

This paper proposed a classification method that can classify the samples of multiple domains based on the cooperation of multiple models. On a sample, our method firstly predicts the domain that may contains this sample based on the output of trained models. Then, our method uses the models that are trained on this domain to classify the label of this sample. Our method can reduce the difficulty of training high performance model while can ensure high accuracy on the samples of multiple domains. Experiments show that our method achieved higher accuracy than the existing methods.

The Classification of Multiple-domain Samples based on Multiple Deep Learning Models

This article proposed a novel classification framework that can classify the samples of multiple domains based on the outputs of multiple models. Different from the existing methods that train single model on all domains, our framework trains multiple models on each domain. On a testing sample, the outputs of all trained models are used to predict the domain of this sample. Then, this sample is classified by the output of models that belong to the predicted domain. Experiments show that our framework achieved higher accuracy than the existing methods. Furthermore, our framework achieves good scalability on multiple domains.

The Classification of Multi-Domain Samples Based on the Cooperation of Multiple Models

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Papers

Classification of Breast Cancer Histopathological Images Using Discriminative Patches Screened by Generative Adversarial Networks

The Optimization of Multi-classifier Ensemble Method Based on Dynamic Weighted Voting

The Classification of Multiple-domain Samples based on Multiple Deep Learning Models

The Classification of Multi-Domain Samples Based on the Cooperation of Multiple Models