Reviewerly/exHarmony huggingface dataset:
Deep Learning-based Histopathological Image Classification of Colorectal Cancer: A Brief Survey of Recent Trends

Early diagnosis is beneficial for treating Colorectal Cancer (CRC) and can improve its curability. The traditional methods of CRC diagnosis generally rely on pathologists, but with the increasing number of CRC patients, manual diagnosis has many shortcomings. In recent years, Deep Learning (DL) has attracted wide attention in various fields. It has higher precision in many complex tasks than traditional machine learning techniques. In particular, DL is widely used in medical image classification, including histopathological images of colorectal cancer. This review summarizes multiple papers on DL for histopathological CRC image classification from 2014 to 2022. These papers discuss the task of deep neural networks and present their challenges and potential development directions for histopathological colorectal cancer image classification.
Which pooling method is better: Max, Avg, or Concat (Max, Avg)

Pooling is a non-linear operation that aggregates the results of a given region to a single value. This method effectively removes extraneous details in feature maps while keeping the overall information. As a result, the size of feature maps is reduced, which decreases computing costs and prevents overfitting by eliminating irrelevant data. In CNN models, the max pooling and average pooling methods are commonly utilized. The max pooling selects the highest value within the pooling area and aids in preserving essential features of the image. However, it ignores the other values inside the pooling region, resulting in a significant loss of information. The average pooling computes the average values within the pooling area, which reduces data loss. However, by failing to emphasize critical pixels in the image, it may result in the loss of significant features. To examine the performance of pooling methods, this study comprised the experimental analysis of multiple models, i.e. shallow and deep, datasets, i.e. Cifar10, Cifar100, and SVHN, and pool sizes, e.g. $2x2$, $3x3$, $10x10$. Furthermore, the study investigated the effectiveness of combining two approaches, namely Concat (Max, Avg), to minimize information loss. The findings of this work provide an important guideline for selecting pooling methods in the design of CNNs. The experimental results demonstrate that pooling methods have a considerable impact on model performance. Moreover, there are variances based on the model and pool size.
HiCTF:A Transformer Model for enhancing Hi-C data resolution

The spatial arrangement revealed by chromatin interactions in Hi-C data is inherently dependent on its resolution. Obtaining high-resolution Hi-C sequencing data remains a significant challenge for many cell lines due to the prohibitive costs of increasing sequencing depth. This study presents an innovative method for improving the resolution of Hi-C data by exploiting the Transformer model. The proposed method leverages the Transformer's inherent attention mechanism to capture the intricate interplay between global chromatin interactions within low-resolution Hi-C matrices. In addition, the method takes a comprehensive view by considering the interrelationship between local attributes that characterize smaller three-dimensional chromatin structures and the overarching context provided by the three-dimensional arrangement of the larger matrix. Significantly different from previous methods involving image segmentation, our method does not use with matrix segmentation, encodes the Hi-C matrix directly, and uses model-based encoding and decoding to achieve a refined data representation.
Segmentation Of Organoid Cultures Images Using Diffusion Networks with Triplet Loss

The present research study explores the use of Diffusion Networks with Triplet Loss for the semantic segmentation of liver organoid cultures images. Since diffusion networks are generative, they encode more abstract and higher-level representations of the training images. In order to adapt such a generative architecture for image segmentation, we implemented triplet loss as its loss objective so that we encourage the diffused predictions to resemble the segmentation maps instead of the original images. The research question is whether the triplet loss is applicable to such an architecture and task, and whether diffusion networks with triplet loss are more performant, and trainable than previous supervised and self-supervised alternatives. This was tested by training the proposed model with a maximum of 500 images, evaluating its F1-score and comparing it to a supervised and self-supervised baseline. Our model (F1=83.7%) significantly outperformed the supervised baseline (F1=64.4%) but was outperformed by the self-supervised one (F1=85.0%). Nevertheless, our model showed greater robustness for typical images but low reliability, as it struggled with ambiguous inputs, causing a skewed distribution of the results and a lower mean score. It was also shown that the proposed model is more trainable than supervised approaches as it needed only 50 training samples to outperform the supervised baseline, which was trained with 114. However, it was less trainable than the self-supervised baseline, whereas it took our model over 200 training samples to match the accuracy of the self-supervised one (trained on 114).
Facial Symmetry Analysis Using Temporal Change in Landmark from A Video Image of 3D Point Cloud

In this paper, we propose two methods to evaluate the facial symmetry from a video image of 3D point clouds. One is the analysis based on the movement of face landmarks. First, we perform the face landmark matching using the FPFH value. It describes the local geometric feature, and hence landmarks in different frames can be matched by comparing FPFH values. After the matching process, we evaluate the facial symmetry from the left-right difference of the temporal changes in landmarks. The other is the analysis based on the center of gravity. We extract the lip region from a point cloud and divide it into subregions. The positions of the center of gravity are calculated for all the subregions, and we evaluate the facial symmetry from the left-right difference of them. As an experimental result, it is shown that the proposal method improves the matching accuracy and evaluates the symmetry like our sense.
AST: Annulus Swin Transformer For Pedestrians Detection Under Top-view Fisheye Image

Fisheye images have a larger field of view than ordinary perspective cameras and can obtain more scene information. Therefore, object detection based on top-view fisheye images has wide applications in security monitoring, passenger flow perception, and industrial detection fields. However, due to the radial distortion symmetry of fisheye images, most current networks still need to adapt to this radial distortion symmetry. Therefore, we proposed the Annulus Swin Transformer(AST) for top-view fisheye images, which includes Annulus Window (AW) and Annulus Position Coding (APC). Traditional rectangular windows can break the radial distortion symmetry in top-view fisheye images, instead of taking advantage of it to improve the network’s perception of distortion. Annulus-Window transforms it into an inner-to-outer annulus window to calculate self-attention, thereby maintaining the distortion symmetry of the fisheye image within each window, allowing the network to perceive better the degree of distortion in the fisheye image and get better detection performance. Due to the varying degree of distortion of images within each annulus, APC introduces an annulus position to work with Annulus window based on the original position encoding, enabling it to contain annulus position information. The experimental results on the dataset SEU-fisheye show that our method improves the neural network’s performance markedly.
Real-time Sidewalk Anomaly Detection from Surveillance Video with A Combination Model

Abnormal event detection is a hot topic in the field of intelligent monitoring, in which the sidewalk anomaly detection plays an important role. Aiming to solve the problems (e.g., time-consuming, laborious, easy to delay, omission, and occlusion) in surveillance video processes for real-time sidewalk anomaly detection, a combination model was proposed in this paper. Firstly, YOLACT++ as a real-time instance segmentation algorithm was used to segment the sidewalk, and then the segmented results were post-processed through ordinary image operations (i.e. dilation and erosion). Finally, YOLOv3 was used to detect illegal vehicles on sidewalk, realizing the real-time monitoring of sidewalk parking and sidewalk illegal driving. As the experiments results showed, accuracy of the trained YOLOv3 was greater than 80% when IOU threshold was set at 0.5, and mAP of the trained YOLACT++ was 0.45. The FPSs of them were above 30, which meet the requirements in practice. On the activity modeling and anomaly detection data set, the combination model was tested, through which the vehicles invading sidewalks were able to be quickly and accurately identified.
Semantic Filtering Global and Local Embeddings Fusion for Few-Shot Image Classification

Neural networks have demonstrated superior performance in image classification tasks, however, acquiring sufficient labeled data for training them in real-world scenarios remains challenging. Few-shot learning (FSL) was proposed to address this challenge in low-data scenarios and has attracted considerable attention in recent years. In few-shot image classification tasks, most of the existing FSL methods focus on designing discriminative network structures for exploring image features, either neglecting semantic information that is crucial for accurate classification, or tending to prioritize either global or local image features. To address this issue, we propose in this work a novel model Semantic Filtering Global and Local embeddings for fusion (SFGL) for few-shot image classification. SFGL effectively integrates both global and local features while leveraging semantic information. Extensive experiments conducted on benchmark datasets demonstrate that our proposed SFGL model outperforms state-of-the-art methods in FSL tasks.
Entity Attribute Alignment Method Based On Attribute Value Distribution

Entity attribute alignment aims to establish correspondence between entity attributes with the same meaning for entities belonging to the same category. It is a crucial foundational technology to construct knowledge graphs based on multi-source heterogeneous data. Existing entity attribute alignment methods mainly infer the alignment relationship of attributes based on the fact that the same attribute of the aligned entity has the same attribute value. However, in most cases, the alignment of entities is unknown in advance, which limits the applicability of such methods. To overcome the dependency on entity alignment relationship information, an attribute alignment method based on attribute value distribution is proposed. In the case of having common entities but without giving their alignment relationships, the proposed method aligns attributes by using the characteristics that the attribute value distribution is similar for the same attribute of entities in different data sources. Firstly, according to the different characteristics of numeric attributes and string attributes, the method uses density estimation and clustering to describe the probability distribution of attribute values for the two types of attributes respectively. Then the cross entropy is used to calculate the similarity of the probability distribution of attribute values of two attributes. Finally, the bidirectional matching approach is used to identify attribute pairs that should be aligned. Experimental results on multiple person entity datasets show that the proposed method can accurately align attributes based on attribute values without providing entity alignment relationships and improves the F1-score by more than 9.0% compared with the related optimal method.
Knowledge Enhanced Fine-grained Biomedical Named Entity Recognition

While Named Entity Recognition (NER) in universal domains has made significant progress, there is still room for improvement in Biomedical Named Entity Recognition (BioNER) as it pertains to specific domains. Unlike the universal domain, NER in specialized domains requires a more detailed categorization of entities. Based on the idea by incorporating knowledge, we propose a method that is better suited for this task. Our method initially employs comparative learning to thoroughly grasp entity category information as additional knowledge. Subsequently, we seamlessly integrate this category information into the NER process. By enhancing knowledge through the incorporation of category information into entity extraction, our method improves the F1 score by 1% compared to existing methods.
Exploring Non-Significant Areas for Weakly Supervised Semantic Segmentation

Semantic segmentation is the process of categorizing all pixels in an image. Given the inherent challenges of attaining fine labels, researchers have recently embraced weak labels to mitigate the annotation burden of segmentation. The current work on weakly supervised semantic segmentation (WSSS) mainly focuses on expanding pseudo-label seeds in the salient regions of the image, but there are also many objects outside the salient area that have not been discovered. In this work, we propose an innovative WSSS method by exploring non-significant areas (ENSA). Specifically, we first utilize multiple local views that are randomly clipped from the input image to extract attention. Then, we design a local-global knowledge transfer module (LGKT) for the global network so that the global network can obtain the complementary attention knowledge of multiple local attention maps through online learning to produce high-quality attention maps. To further explore the non-significant areas of complex images, we adopt a NSRM module to generate masked labels. Comprehensive experiments on the PASCAL VOC 2012 dataset illustrate that we achieve state-of-the-art performance compared to existing work.
Sentiment Analysis of Weibo Text Using a Deep Combination Model

Social media platforms like Weibo have become the primary means for the public to express emotions and opinions. However, sentiment analysis with manual interpretation is not applicable in practice, due mainly to the vast amount of user-generated contents. This paper adopted an approach for text sentiment analysis, called CNN-BiLSTM model, through combining Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM). The CNN-BiLSTM model has been proved more effective in accurately determining the sentiment orientation within complex Weibo comments under various contexts. By employing the CNN-BiLSTM model, this research aimed to enhance the performance of sentiment analysis tasks, leading to improved accuracy and efficiency. Results demonstrated remarkable success, with the CNN-BiLSTM model achieving an accuracy of 98.16%, which suggested the efficacy of deep learning techniques in addressing the challenges of sentiment analysis for Weibo text. However, it is important to acknowledge some limitations in this study, such as the oversimplified sentiment labels, the relatively limited dataset scope, and the absence of consideration for multiple emotional nuances. To overcome these shortcomings, future endeavors should focus on introducing more nuanced sentiment labels, expanding the dataset's coverage, and accounting for additional factors influencing sentiment attitudes. Furthermore, more advanced and reliable deep learning models are required for sentiment attitude prediction.
Robust Graph Regularized Auto-Encoders by Cross Entropy Penalty

Graph regularized Auto-Encoder (GAE) is an AE variant that incorporates with manifold learning. Using graph Laplacian as regularization towards encodings, GAE could preserve locality of original data in low dimensions, achieving better clustering and visualization results than original AE and its other variants. Nevertheless, graph Laplacian in GAE takes 2-norm loss function to penalize locality of encodings, likely leading to instability. Pinpointing at this, in this paper we propose a robust graph regularized auto-encoder (RGAE). Instead of graph Laplacian, RGAE adopts cross entropy for penalization, pursuing consistency between locality of original data and that of its low-dimensional encodings. In particular, RGAE is less parameterized, since it takes no local weights into account. Experiments on benchmark datasets reveal that RGAE is on the whole more performant than GAE at clustering along with varied encoding dimensions, and achieves better visualization results.
Adaptive Blind Super-resolution Model based on Degradation Representation

Most CNN-based super-resolution methods are normally designed based on the assumption that low-resolution images have a fixed degradation pattern. When the actual degradation differs from its assumption, the performance of these methods will severely degrade. In order to deal with this problem, most of the previous methods adopt the way of predicting the blur kernel to reconstruct the SR image. However, this type of methods needs to accurately predict the blur kernel, where the error propagation caused by the estimation deviation of the blur kernel cannot be avoided. To address these issues, we propose an adaptive blind super-resolution model based on degenerate representation (ABSRM) in this paper. First, the shallow feature extraction module extracts the original blurriness features from the low-resolution (LR) image. Second, the degradation representations generator generates degradation representations to distinguish various degradations in the representation space instead of specific blur kernels. Third, the adaptive reconstruction module flexibly handles various degradations to reconstruct clearer images based on different degradation representations. Extensive experiments show that our ABSRM shows advantages compared with the state-of-the-art blind super-resolution methods.
A Deep Learning Sentiment Analysis Method based on ERNIE and Modified DPCNN

Pre-training models such as BERT, RoBERTa, and ERNIE have gained popularity in natural language processing (NLP). Within this domain, sentiment analysis has become an important task. People express their emotional states through their comments on social media, and sentiment analysis involves using NLP techniques to parse these comments. In the study, we propose an advanced sentiment classification model that combines ERNIE with a modified DPCNN. Our model achieved a Macro-F1 score of 87.03% on the publicly available dataset EmoInt, surpassing the performance of the six baseline models, indicating that the proposed model has competitive performance.
Small Object Detection with YOLOv8 Algorithm Enhanced by MobileViTv3 and Wise-IoU

Small object detection is an important and challenging task in computer vision, with widespread applications in fields such as remote sensing, autonomous driving, and security. However, due to factors such as small size, indistinct features, and complex backgrounds of small objects, traditional convolutional neural network (CNN)-based object detection algorithms often struggle to effectively detect small objects. To address issues such as insufficient feature extraction, suboptimal anchor box matching, and unbalanced loss functions, an improved YOLOv8 algorithm is proposed. This algorithm enhances the detection accuracy of small objects by adding a lightweight visual transformer, MobileViTv3, which can enhance feature representation capabilities, and a Wise-IoU loss function that adaptively adjusts the ratio of positive and negative samples and regression coefficients. Results show that the improved YOLOv8 algorithm achieves an increase of 2.5%, 2.1%, and 2.9% in precision, recall, and mAP respectively on the public remote sensing dataset VisDrone2019 and 2.4%, 2.7%, and 3.5% on DOTA compared to the original algorithm. It can effectively improve the accuracy of small object detection in complex scenarios while meeting the requirements for detection speed.
Class-Aware Feature Regularization for Semantic Segmentation

In this paper, to address the problem of intra-class consistency and inter-class variation in the deep convolutional neural network (CNN) based methods for semantic segmentation of images, we propose a class-aware feature regularization strategy to revise the features extracted by a deep convolutional neural network, without any change of the original network structure. A pixel-context similarity term is proposed to measure the consistency between feature vectors of pixels and class centers, which guarantees the intra-class consistency of pixels in the interior of an object and is supervised by a One-Hot label to preserve the inter-class variation of different objects. Based on the similarity term, we design a lightweight and efficient plug-in loss term to ensure that the features yielded by a deep CNN possess the quality of intra-class consistency and inter-class variation. As our ideal can be fulfilled effectively by the proposed plug-in loss term, we can simply incorporate it into a CNN-based segmentation model without changing the model structure. The effectiveness of the proposed strategy is proved by incorporating the loss term into some state-of-the-art segmentation models on Cityscapes and ADE20K datasets.
MCA-Net: Multi-scale Context-aware Aggregation Network for Medical Image Segmentation

Segmentation is still a challenging task in medical image analysis, especially for multi-organ segmentation, in which the volume difference of each organ is large and the shape is irregular. In this paper, we propose a Multi-scale Context-aware Aggregation Network (MCA-Net) for multi-organ segmentation. MCA-Net mainly contains three major components: an encoder, a Multi-scale Context-aware Aggregation module, and a decoder. A hybrid encoder combining CNN and Transformer is adopted to compensate for the shortcomings of convolution operation. The multi-scale context-aware aggregation module is composed of multi-scale residual atrous convolutional (MRAC) block and residual pyramid pooling (RPP) block. Also, Convolutional Block Attention Module (CBAM) block is introduced to enhance important features and suppress unnecessary ones. Additionally, to reduce the loss of low-level information, cascaded up-sampling is employed to recover the resolution of original image, and skip connections are introduced in the middle to further integrate the feature information. Our method achieves competitive results on multi-organ segmentation and cardiac segmentation datasets compared with the state-of-the-art approaches.
Optical Motion Capture Equipment Installation Training Web3D Virtual Simulation System

The optical motion capture devices are widely used in various fields such as new media, metaverse, film, and gaming. However, due to its high price, limitations in installation caused by temporal and spatial conditions, among other reasons, the installation and training of optical motion capture equipment are challenging. To address this issue, this paper proposes an optical motion-capturing device installation training system based on Web3D virtual simulation technology. This system is divided into three functional modules: interactive webpages, virtual simulation experiments, and grade and information management. This system uses Unity3D and 3DsMax to simulate the installation and dismantling process of optical motion capture devices. It utilizes recording functions in the Webgl2.0 framework to record and playback the experimental process. The system manages step-by-step experimental data, such as performance records, using a database and designs interactive web pages using. The experimental results show that the system helps users quickly master the methods and skills of installing and debugging optical motion capture equipment.