The ensemble deep learning model for novel COVID-19 on CT images.

We present the first comprehensive video polyp segmentation (VPS) study in the deep learning era. Over the years, developments in VPS are not moving forward with ease due to the lack of large-scale fine-grained segmentation annotations. To address this issue, we first introduce a high-quality frame-by-frame annotated VPS dataset, named SUN-SEG, which contains 158,690 colonoscopy frames from the well-known SUN-database. We provide additional annotations with diverse types, i.e., attribute, object mask, boundary, scribble, and polygon. Second, we design a simple but efficient baseline, dubbed PNS+, consisting of a global encoder, a local encoder, and normalized self-attention (NS) blocks. The global and local encoders receive an anchor frame and multiple successive frames to extract long-term and short-term spatial-temporal representations, which are then progressively updated by two NS blocks. Extensive experiments show that PNS+ achieves the best performance and real-time inference speed (170fps), making it a promising solution for the VPS task. Third, we extensively evaluate 13 representative polyp/object segmentation models on our SUN-SEG dataset and provide attribute-based comparisons. Finally, we discuss several open issues and suggest possible research directions for the VPS community. 

/pdf/video-polyp-segmentation-a-deep-learning-perspective-3sr2l1rw.pdf

Video Polyp Segmentation: A Deep Learning Perspective

Change detection methods play an indispensable role in remote sensing. Some change detection methods have obtained a fairly good performance by introducing attention mechanism on the basis of the convolutional neural network (CNN), but identifying intricate changes remains difficult. In response to these problems, this article proposes a new model for detecting changes in remote sensing, namely, MTCNet, which combines the advantages of multi-scale Transformer with the convolutional block attention module (CBAM) to improve the detection quality of different remote sensing images. On the basis of traditional convolutions, the Transformer module is introduced to extract bitemporal image features by modeling contextual information. Based on the Transformer module, a multi-scale module is designed to form a multi-scale Transformer, which can obtain features at different scales in bitemporal images, thereby identifying the changes we are interested in. Based on the multiscale Transformer module, the CBAM is introduced. The CBAM is split into a spatial attention module (SAM) and a channel attention module (CAM), which are applied to the front and back ends of the multi-scale Transformer respectively. Spatial information and channel information of feature maps are modeled separately. In this article, the validity and efficiency of the method are verified by a large number of experiments on the LEVIR-CD dataset and the WHU-CD dataset. 

https://ieeexplore.ieee.org/ielx7/4609443/4609444/09855775.pdf

A CBAM Based Multiscale Transformer Fusion Approach for Remote Sensing Image Change Detection

Microorganisms or microbes comprise majority of the diversity on earth and are extremely important to human life. They are also integral to processes in the ecosystem. The process of their recognition is highly tedious, but very much essential in microbiology to carry out different experimentation. To overcome certain challenges, machine learning techniques assist microbiologists in automating the entire process. This paper presents a systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms. This review investigates certain research questions to analyze the studies concerning image pre-processing, feature extraction, classification techniques, evaluation measures, methodological limitations and technical development over a period of time. In addition to this, this paper also addresses the certain challenges faced by researchers in this field. Total of 100 research publications in the chronological order of their appearance have been considered for the time period 1995-2021. This review will be extremely beneficial to the researchers due to the detailed analysis of different methodologies and comprehensive overview of effectiveness of different ML techniques being applied in microorganism image recognition field.

/pdf/machine-learning-and-deep-learning-based-computational-3qvtzg2hzg.pdf

Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments

Colonic polyps are more likely to be cancerous, especially those with large diameter, large number and atypical hyperplasia. If colonic polyps cannot be treated in early stage, they are likely to develop into colon cancer. Colonoscopy is easily limited by the operator’s experience, and factors such as inexperience and visual fatigue will directly affect the accuracy of diagnosis. Cooperating with Hunan children’s hospital, we proposed and improved a deep learning approach with global average pooling (GAP) in colonoscopy for assisted diagnosis. Our approach for assisted diagnosis in colonoscopy can prompt endoscopists to pay attention to polyps that may be ignored in real time, improve the detection rate, reduce missed diagnosis, and improve the efficiency of medical diagnosis. We selected colonoscopy images from the gastrointestinal endoscopy room of Hunan children’s hospital to form the colonic polyp datasets. And we applied the image classification method based on Deep Learning to the classification of Colonic Polyps. The classic networks we used are VGGNets and ResNets. By using global average pooling, we proposed the improved approaches: VGGNets-GAP and ResNets-GAP. The accuracies of all models in datasets exceed 98%. The TPR and TNR are above 96 and 98% respectively. In addition, VGGNets-GAP networks not only have high classification accuracies, but also have much fewer parameters than those of VGGNets. The experimental results show that the proposed approach has good effect on the automatic detection of colonic polyps. The innovations of our method are in two aspects: (1) the detection accuracy of colonic polyps has been improved. (2) our approach reduces the memory consumption and makes the model lightweight. Compared with the original VGG networks, the parameters of our VGG19-GAP networks are greatly reduced.

/pdf/an-improved-deep-learning-approach-and-its-applications-on-3htf6m9082.pdf

An improved deep learning approach and its applications on colonic polyp images detection

As a lightweight deep neural network, MobileNet has fewer parameters and higher classification accuracy. In order to further reduce the number of network parameters and improve the classification accuracy, dense blocks that are proposed in DenseNets are introduced into MobileNet. In Dense-MobileNet models, convolution layers with the same size of input feature maps in MobileNet models are taken as dense blocks, and dense connections are carried out within the dense blocks. The new network structure can make full use of the output feature maps generated by the previous convolution layers in dense blocks, so as to generate a large number of feature maps with fewer convolution cores and repeatedly use the features. By setting a small growth rate, the network further reduces the parameters and the computation cost. Two Dense-MobileNet models, Dense1-MobileNet and Dense2-MobileNet, are designed. Experiments show that Dense2-MobileNet can achieve higher recognition accuracy than MobileNet, while only with fewer parameters and computation cost.

/pdf/a-novel-image-classification-approach-via-dense-mobilenet-2089bar60z.pdf

A Novel Image Classification Approach via Dense-MobileNet Models

With the presentation of super-resolution convolutional neural network, deep learning approach was applied to image super-resolution reconstruction for the first time. By using convolutional neural network, the deep learning approaches can directly learn the mapping relationship between the low-resolution image and high-resolution image, and have achieved better reconstruction effects than the traditional image super-resolution reconstruction methods. Subsequently, a series of improved deep learning approaches have been proposed, and the reconstruction effects have been improved continuously. This paper systematically summa rizes the image super-resolution reconstruction approaches based on deep learning, analyzes the characteristics of different models, and compares the main deep learning models based on the experiments. Furthermore, based on deep learning model, the future research directions of the image super-resolution reconstruction methods based on deep learning models are reasonably predicted.

Brief Survey of Single Image Super-Resolution Reconstruction Based on Deep Learning Approaches

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A Novel Image Classification Approach via Dense-MobileNet Models

An improved deep learning approach and its applications on colonic polyp images detection

Brief Survey of Single Image Super-Resolution Reconstruction Based on Deep Learning Approaches