Reviewerly/exHarmony huggingface dataset:
Named Entity Recognition for Long COVID Biomedical Literature by Using Bert-BiLSTM-IDCNN-ATT-CRF Approach

In recent years, with the exploration of pathological mechanisms and treatments of Long COVID, there has been a dramatic increase in related scientific publications. Effective extraction of key information from these texts is of great importance for public health and research progress. In the Long COVID context, Named Entity Recognition (NER) can be used to identify disease names as well as symptoms, which can help to analyze the sequelae caused by COVID-19 and its relationship with other diseases. Distinguished from molecular biomedical text mining, which focuses on the identification of entities such as genes, proteins, and chemistries and their relationships, Long COVID text mining faces problems such as the lack of publicly labeled datasets and the heavy workload of manual annotation. Moreover, due to the strong domain characteristics of Long COVID relevant named entities, models and methods that have achieved great performance in the generic domain will have significantly degraded named entity recognition performance on this domain. Based on the above problems, we constructed a Long COVID literature abstract NER dataset (LNER) and proposed a Long COVID biomedical literature NER model Bert-BiLSTM-IDCNN-ATT-CRF (BBIAC). First, the BERT-BiLSTM-CRF model is constructed on the LNER dataset. Then, the inflated convolutional neural network (IDCNN) is added between the BiLSTM and the CRF layers to obtain the local features in the text sequences. Finally, feature enhancement is performed by fusing the features of global and local information using the attention mechanism. The experimental results show that the method proposed in this paper for Long COVID literature can accurately extract the characteristic information of Long COVID symptoms and diseases, and has better performance compared to other baseline models.
MEGP: Dual feature extraction network for Chinese medical entity recognition

Named Entity Recognition(NER) is a key technology for information extraction. Chinese medical text contains a high number of nested entities, making entity recognition more complex compared to ordinary text. Previous methods for recognizing entities of Chinese medical text often ignore the nested entity parts and directly use sequence labeling methods to recognize the whole entity. Therefore, this paper proposes a named entity recognition model for Chinese medical text that effectively improves the accuracy of nested entities. The model based on dual features extraction network and Global Pointer. The different features are obtained by pre-training model RoBERTa-wwm and four-corner method and combined with BiLSTM to obtain fused feature vectors. Then using the Global Pointer to decode the features to obtain the recognition results. The experimental results show that the proposed model has an average F1-score of 71.6% on the CMeEE dataset, which performs better than the benchmark models. The proposed model is significantly superior to the existing methods in terms of nested-type entity recognition performance, and the overall accuracy is increased by 8.96% compared with the benchmark models.
Optimizing Network Architectures for Ultrasound Cardiac Segmentation

Cardiac image segmentation is critical for medical diagnosis and treatment planning. Traditional approaches often face accuracy challenges. In this study, we propose a deep learning-based method that incorporates architectural improvements and optimization techniques to overcome these limitations. Our method integrates skip connections, a spatial attention mechanism, and label smoothing for enhanced segmentation performance. Experimental results on the CAMUS dataset show that our approach surpasses baseline models, achieving superior segmentation accuracy. Specifically, our method increases the mean Intersection over Union (mIoU) from 0.8141 (U-Net) to 0.8428 (Residual Attention U-Net) and the mean Dice score from 0.8948 (U-Net) to 0.9127 (Residual Attention U-Net). The proposed method has potential applications in medical diagnosis, disease prevention, and treatment planning, emphasizing its practical significance in cardiac image segmentation.
Named Entity Recognition in Electronic Medical Records Based on Bidirectional Long Short-Term Memory-Conditional Random Field

In most hospitals in China, the actions of medical staff, patients' conditions, and treatment methods for diseases all need to be recorded in a system for easy management and viewing. This system is called electronic medical record(EMR). Due to the large number of medical institutions in China, EMR contains a large amount of valuable information. The purpose of this study is to use deep learning to automatically identify key words in EMR. This study uses a Bi-directional Long Short-Term Memory (BiLSTM) network to extract sequence information, and uses a Conditional Random Field (CRF) to solve the label mapping problem. An open-source dataset is used to train and test the model. In the experiment, we collected multiple experimental data and evaluated the model's performance. The results show that the model can be well applied to the entity recognition task in EMR.
Visualization of Chinese herbal medicine data based on WebGL

With the development of information technology, data visualization has become a research issue because it can help people quickly understand the information in the data center and improve decision-making accuracy. In addition, Chinese herbal medicine culture is one of the intangible cultural heritages in China and has a very long history. Therefore, we design and implement a data visualization system for Chinese herbal medicine. Then we explain the technology used in the whole system. Finally, we introduce the process of building a 3D data visualization system for Chinese herbal medicine, which promotes the dissemination of Chinese herbal medicine culture.
3D-2D Medical Image Registration Technology and Its Application Development: a Survey

Due to the lack of spatial topology information in 2D medical images, doctors cannot accurately identify tissues in patients during clinical diagnosis and treatment. 3D medical images can make up for 2D images' lack of spatial topolospatial and topological symmetry. Registration of 3D images to 2D image space can enhance original 2D medical images. 3D-2D medical image registration technology plays a key role in radiotherapy, image-guided surgery and preoperative lesions diagnosis. This study examined 48 articles on 3D-2D medical image registration. The article explains how 3D-2D medical image registration is used in clinical and preoperative diagnosis. The key techniques of 3D-2D medical image registration were analyzed. Finally, the paper summarizes the future development trends and challenges of 3D-2D medical image registration.
Self-supervised comparative learning based improved multiple instance learning for whole slide image classification

The huge pixels of the Whole slide image (WSI) and the lack of pixel-level annotation make WSI classification tasks such as tumor and subtype classifications face great challenges. Multiple instance learning (MIL) is introduced as a powerful learning tool for WSI classification tasks, which only requires slide-level labels to achieve good results. In this paper, we introduce an efficient transformer multiple instance learning model based on large kernel attention LKA, using self-supervised contrast learning to extract instance features and combining local and long-range attentional information to achieve state-of-the-art results on open datasets for two different tasks. Our model can cope well with various WSI classification tasks such as tumor classification, subtypes classification, etc. The AUCs for tumor classification on the CAMELYON16 datasets and cancer subtypes classification on TCGA-NSCLC datasets can reach 0.9778 and 0.9754, respectively.
Calibration of Freehand Ultrasound Based on YOLOv8x Keypoints Detection Network

Ultrasound 3D reconstruction could be used for realizing non-ionizing radioactive intraoperative ultrasound guidance, in which spatial calibration of the ultrasound probe was a critical step. In this study, a freehand ultrasound approach was used and an N-wire phantom was used for spatial calibration. Instead of manually picking the bright fiducial points in the images as the previous studies reported in the literatures, this work identified fiducial points automatically based on the YOLOv8x keypoints detection, which improved the precision and efficiency of probe spatial calibration over the manual methods. The experimental results showed that the calibration accuracy was 2.02mm, and the processing time on each frame was 0.075s.
Accurate liver tumors segmentation using semantic localization and centroid region growth

The accurate segmentation of liver tumors is the premise of judging the scope of liver tumors and formulating treatment plan. However, the traditional methods of liver tumor segmentation have the problems of high complexity and low precision. In order to address these issues, we propose a novel semantic localization and centroid region growth approach in this work. Firstly, the 3D U-Net+ deep learning model is adopted to extract features and locate the liver. Secondly, appropriate initial seed points are selected in the liver by the centroid region growth algorithm, and the tumor regions are gradually grown and segmented according to the growth criteria. To enhance the precision of segmentation even further, post-processing steps are introduced to remove the holes in the tumor region and perform boundary smoothing. The experimental outcomes demonstrate that the algorithm presented attains exceptional segmentation precision and robustness when applied to the LITS2017 dataset. Therefore, the algorithm has the potential to be used as a reliable tool in clinical practice to provide physicians with accurate liver tumor localization and segmentation results.
Design of Ward Medical Monitoring System Based on Internet of Things

In this paper, an IoT (Internet of Things)-based medical monitoring system for wards is designed to address the current problems of too many patients in hospital wards, few healthcare workers, and the heavy workload of healthcare workers, who are not able to deal with the needs of each patient in a timely manner. The system automatically detects the patient's temperature, heart rate and infusion status by using a temperature detection sensor, a heart rate detection sensor and an infusion status detection sensor, the data acquired by the sensors are transmitted to the STM32 Micro Control Unit MCU through serial communication, and the MCU processes, calculates the data and displays them on the OLED and on the cell phone. In the meantime, with the help of WIFI module, the MCU communicates with the cell phone through the cloud platform relay station. When the patient's body temperature or heart rate is abnormal, the value is not in the preset value range, it will issue an alarm message to remind the patient and the medical staff, the medical staff is kept abreast of the patient's condition and treats the patient at the first opportunity. When the patient's infusion is almost finished, the medical staff will also receive prompt information, so as to add drugs or remove the needle timely. In addition, the patient can choose to adjust the angle of the hospital bed by pressing the button or on the cell phone, which is convenient and more humanized. This system has been physically welded, produced, programmed and debugged, and each functional module has been physically tested to realize the corresponding functions. This is of notable practical significance for the intelligence of medical system, which will improve medical efficiency and save the investment of medical personnel.
Design of Intelligent Infusion System for Wards Based on Internet of Things

This paper designs an intelligent infusion system for wards based on the Internet of Things. The system uses STM32 Micro Control Unit(MCU) as the control core, and modules such as sensors, buttons, OLED display, WIFI, SMS sending as peripheral input/output devices. The patient's body temperature and infusion speed are detected by infrared temperature sensor and droplet speed sensor respectively, and the system inputs the sensor detection signals into the MCU, which analyzes, calculates, and processes and then displays the relevant information on the OLED and the nurse's cell phone, including the patient's body temperature, the infusion speed, the remaining amount of drug, and the name of the medicine. Medical staff can monitor the patient's temperature and infusion situation in real time on the cell phone. When the patient's body temperature exceeds the preset value of 37.5℃ or the remaining amount of drug is less than 20ml, the buzzer will alert the patient, and also send reminding information to the nurse's cell phone, so that the medical staff can treat the patient in a timely manner, and add medicine or remove the needle in time. The patient can press the call button if he/she has any needs, and the patient's call request will be automatically received on the nurse's cell phone. The system will also send the list of drugs to be infused by the patient on the same day to the patient's family members' cell phone through the texting module. After physical debugging, this system achieves the required functions and has the characteristics of simple operation, human-computer interaction, alarm reminder and remote monitoring, which is of strong practical significance and practical value for medical intelligence.
Segmentation of Multi-Organ Functional Tissue Units Using UNet-EfficientNet-B8

Segmentation of functional tissue units aims to discriminate tissue units of epithelium, glandular cavity, and fiber from an image, which helps to enhance humans' understanding from the cell level and can fundamentally and strongly support future research in the field of Biotechnology. However, there are no easily discernible boundaries between functional and non-functional units in tissue images, making it difficult for machines to do such tasks. Based on common ideas from convolutional neural networks, the structural advantages of UNet and EficientNet are combined to create a new segmentation model for organ functional tissue units. In our experiment, UNet and EfficientNet are fused into a new model that extracts features with the help of the pre-trained EficientNet parameters to improve the ability of feature extraction. Besides, the combination of multi-scale features in the network is realized via the skip connection, and the segmentation accuracy of the model is thus improved. Finally, our proposed model is compared with other models by using the metrics of the Dice Similarity Coefficient (DSC). The result shows our UNet-EfficientNet-B8 owns the highest DSC of 0.721 among UNet-ResNet-50, UNet-ResNet-101, and UNet-Se_ResNet-101.
Research on Chest Medical Image Modality Transfer Using GANs

MRI plays a crucial role in clinical diagnosis and lesion analysis. MR images from different sequences provide richer information, aiding healthcare professionals in making accurate clinical diagnoses. This paper proposes a self-supervised Generative Adversarial Network (SC-GAN) framework, which synthesizes Apparent Diffusion Coefficient (ADC) images from T2 Turbo Inversion Recovery Magnitude (T2 TIRM) images. The SC-GAN model introduced in this paper does not require a paired dataset, instead utilizing self-features as supervisory information to synthesize high-quality MR images. Experimental results indicate that, compared to existing GAN-based methods, this approach achieves higher levels of perceptual quality and tissue detail in the generated chest T2 TIRM images.
Pancreatic Tumor Segmentation Based on 3D U-Net with Densely Connected Atrous Spatial Pyramid Module and Attention Module

The segmentation of CT images of pancreatic tumors is of great significance for the diagnosis and treatment of pancreatic tumors. Due to the small size and irregular shape of pancreatic tumors, precise segmentation of pancreatic tumor CT images using neural networks remains a challenge in machine learning. At present, the dice coefficient, MIoU coefficient, and Precision coefficient of neural network for pancreatic tumor CT image segmentation are only 50%, 30%, and 50% at most, respectively. There is a lot of room to improve the segmentation effect. This paper proposes a CT image segmentation method for pancreatic tumors based on a 3D U-Net network and integrating Densely Connected Atrous Spatial Pyramid Module (DenseASP Module) and Attention Module (AM). In order to effectively extract small target feature information, this network adds DenseASP to each layer of the downsampling section; At the same time, in order to filter irrelevant information and enhance the extraction of target area information, we add Attention Gates (AG) to each layer of the upsampling section, and Channel Attention Module (CAM) to the skip connection section. The Attention Gates and Channel Attention Modules together constitute the Attention Module (AM) of our network. This paper selects dice coefficient, MIoU coefficient, Precision coefficient as the evaluation index of segmentation effect, and uses the pancreas dataset MSD Pancreas to evaluate our model. The experimental results show that the various segmentation evaluation indicators of the model in this paper have improved by about 2 percentage points compared to the current neural network.
Differentiable Attention Unet-like Nerual Architecture Search for Multimodal Magnetic Resonance Imaging-based Glioma Segmentaion DAUNAS for Multimodal MRI-based Glioma Segmentation

The utilization of artificial intelligence has had a profound impact on diverse areas within the medical field day by day. Specifically, the identification and management of gliomas, a specific category of brain tumors characterized by a challenging prognosis, heavily depend on the computer-assisted analysis of magnetic resonance imaging (MRI) scans. In the present study, we introduce a novel approach based on neural architecture search (NAS) for accurately segmenting brain tumors utilizing multimodal volumetric MRI scans. Our method employs three classes of candidate operations for different cells, with each operation having a learnable probabilistic parameter. By iteratively updating the operation weights and other network parameters, we discover optimal structure for the encoder and decoder cells. Additionally, we introduce an attention module connection to the automatic search, complementing the connection between the encoder cells and decoder cells for brain MRI processing. Through extensive experiments conducted on the BraTS 2019 dataset, we validate the effectiveness and scalability of our proposed algorithm. Our approach not only relieves the burden of manual architecture design but also achieves competitive performance in terms of brain tumor segmentation.
Deep feature selection for cervical cancer

Cervical cancer is second only to breast cancer, early screening is beneficial to the prevention and treatment of cervical cancer as soon as possible. However, due to the high dimensional and complex characteristics of multi-omics data, the model generalization ability is still low in cancer prediction. To solve this problem, this paper proposes a new deep feature selection algorithm, which is based on Lasso penalty estimation, feature selection is performed and a neural network model is embedded to improve the generalization ability of cervical cancer omics data. Finally, through the comparison and survival analysis with different classifiers, it is proved that the algorithm has good performance in feature selection and improving data generalization ability.
Unpaired Bone Marrow Smears Virtual Staining Using Content and Attention-Guided Generative Adversarial Networks: UBMSVStain Using Content and Attention-Guided Generative Adversarial Networks

Morphological examination of bone marrow cells is the standard for the diagnosis of Acute Lymphoblastic Leukemia (ALL). However, the preparation of bone marrow smears requires tedious staining steps, trained pathologists, and a professional experimental environment. Hence, a method that can transform bright-field microscopy images of unstained bone marrow cell smears into Wright & Giemsa (W&G)-stained images of the same samples is essential. However, paired image data is hardly available. In addition, existing unsupervised methods have limitations on datasets of entirely unpaired and complex texture images. This paper proposes an Unpaired Bone Marrow Smears Virtual Staining (UBMSVStain) method, in which a Content And Attention-Guided Staining (CAGS) module is designed to enhance the features after the skip connections and improve the preservation of structural information. All experimental results show that UBMSVStain not only achieves virtual staining of bone marrow smears but also has superior performance.
Bi-omics prognostic model for invasive ductal carcinoma using deep learning

Invasive ductal carcinoma is a common subtype of breast cancer, and current prognostic models are mainly single-omics models. We aimed to develop a bi-omics model combining cancer-associated fibroblast(CAF) gene expression and Whole-field digital slice image(WSI) features to improve the accuracy of predicting prognostic effect. A total of 491 patients were included in the experiment and were randomized into training and validation sets. CAF genes associated with patient survival were screened by gene set enrichment analysis (GSEA), differential expression analysis and Cox regression. Whole-field digital slice image features were extracted using deep learning-based U²Net model and artificial feature approaches, and a bi-omics prognostic model was established by multi-factor Cox regression, and finally survival analysis and AUC values were applied to validate the model accuracy. The prognostic prediction performance of the combined bi-omics model was significantly improved compared to the single-omics model. This study highlights the potential of integrating CAF gene expression and WSI features to better predict the prognosis of IDC patients and provide valuable information for clinical decision making and personalized treatment strategies.
KeplAR: All-around Augmented Reality Smart Glasses

KeplAR is innovative augmented reality glasses with thin, light weight, high optical efficiency, and wide field of view optics. Here we introduce PinTILT which is the key optical technology realizing these features
Fact Checking with Insufficient Evidence

Automating the fact checking (FC) process relies on information obtained from external sources. In this work, we posit that it is crucial for FC models to make veracity predictions only when there is sufficient evidence and otherwise indicate when it is not enough. To this end, we are the first to study what information FC models consider sufficient by introducing a novel task and advancing it with three main contributions. First, we conduct an in-depth empirical analysis of the task with a new fluency-preserving method for omitting information from the evidence at the constituent and sentence level. We identify when models consider the remaining evidence (in)sufficient for FC, based on three trained models with different Transformer architectures and three FC datasets. Second, we ask annotators whether the omitted evidence was important for FC, resulting in a novel diagnostic dataset, SufficientFacts, for FC with omitted evidence. We find that models are least successful in detecting missing evidence when adverbial modifiers are omitted (21% accuracy), whereas it is easiest for omitted date modifiers (63% accuracy). Finally, we propose a novel data augmentation strategy for contrastive self-learning of missing evidence by employing the proposed omission method combined with tri-training. It improves performance for Evidence Sufficiency Prediction by up to 17.8 F $${ }_{1}$$ score, which in turn improves FC performance by up to 2.6 F $${ }_{1}$$ score (The content in this chapter has been reprinted, with permission, from the original work in Atanasova et al. (2022)).