Objective To automatically segment diabetic retinal exudation features from deep learning color fundus images. Methods An applied study. The method of this study is based on the U-shaped network model of the Indian Diabetic Retinopathy Image Dataset (IDRID) dataset, introduces deep residual convolution into the encoding and decoding stages, which can effectively extract seepage depth features, solve overfitting and feature interference problems, and improve the model's feature expression ability and lightweight performance. In addition, by introducing an improved context extraction module, the model can capture a wider range of feature information, enhance the perception ability of retinal lesions, and perform excellently in capturing small details and blurred edges. Finally, the introduction of convolutional triple attention mechanism allows the model to automatically learn feature weights, focus on important features, and extract useful information from multiple scales. Accuracy, recall, Dice coefficient, accuracy and sensitivity were used to evaluate the ability of the model to detect and segment the automatic retinal exudation features of diabetic patients in color fundus images. Results After applying this method, the accuracy, recall, dice coefficient, accuracy and sensitivity of the improved model on the IDRID dataset reached 81.56%, 99.54%, 69.32%, 65.36% and 78.33%, respectively. Compared with the original model, the accuracy and Dice index of the improved model are increased by 2.35% , 3.35% respectively. Conclusion The segmentation method based on U-shaped network can automatically detect and segment the retinal exudation features of fundus images of diabetic patients, which is of great significance for assisting doctors to diagnose diseases more accurately.
Recent studies have introduced attention models for medical visual question answering (MVQA). In medical research, not only is the modeling of “visual attention” crucial, but the modeling of “question attention” is equally significant. To facilitate bidirectional reasoning in the attention processes involving medical images and questions, a new MVQA architecture, named MCAN, has been proposed. This architecture incorporated a cross-modal co-attention network, FCAF, which identifies key words in questions and principal parts in images. Through a meta-learning channel attention module (MLCA), weights were adaptively assigned to each word and region, reflecting the model’s focus on specific words and regions during reasoning. Additionally, this study specially designed and developed a medical domain-specific word embedding model, Med-GloVe, to further enhance the model’s accuracy and practical value. Experimental results indicated that MCAN proposed in this study improved the accuracy by 7.7% on free-form questions in the Path-VQA dataset, and by 4.4% on closed-form questions in the VQA-RAD dataset, which effectively improves the accuracy of the medical vision question answer.
Objective To separate each protein band from the nerve regeneration conditioned fluid(NRCF）and to study whether there are somenew and unknown neurotrophic factors in the protein bands with a relative molecular mass of 220×103. Methods The silicone nerve regenerationchambers were formed in the sciatic nerve of the 25 New Zealand rabbits (weight,1.8-2.5 kg), and NRCF was taken from it at 1 week after operation. The Nativepolyacrylamide gel electrophoresis (Native-PAGE) was used for separating the proteins from NRCF and detecting the relative molecular mass. The Western blot and ELISA were used to observe whether the protein bands ［220×103 (Band a), (20-40)×103(Band c)］ of NRCF could combine with the antibody of the known antibody of neurotrophic factor (NTF):nerve growth factor(NGF), glial cell-derived neurotrophic factor(GDNF), brainderived neurotrophic factor(BDNF), neurotrophin 3(NT-3)， NT-4, ciliang neurotrophic factor(CNTF). Results Separated by Native-PAGE, NRCF mainly contained two protein bands:Band a had a relative molecular mass about 220×103, and Band c had a relative molecular mass about (20-40)×103. Band a could not combine with the antibodies of the NGF, BDNF, CNTF, and NT-3, but could combine with the antibody of NT-4.Band c could combine with the antibodies of NGF, BDNF, CNTF and NT-3, but could not combine with the antibodies of NT-4 and GDNF. Conclusion The protein bands with a relative molecular mass of 220×103 have ber neurotropic and neurotrophic effects than the protein bands with a relative molecular mass of (20-40)×103, which contains NGF,CNTF, etc. NT-4 just has a weak or no effect on the sympathetic neurone. This indicates that there is a new NTF in the protein bands with a relative molecular mass of 220×103, which only combines with the antibody of NT-4.
Diabetic retinopathy (DR) is a common ocular complication in diabetic patients, which is chronic and progressive and seriously impairs visual acuity. The rapid occurrence and progress of cataract in diabetic patients is also one of the important reasons for visual impairment in DR patients. Compared with non-diabetic patients, diabetic patients have higher risk of complications after cataract surgery. Studies have shown that anti-vascular endothelial growth factor (VEGF) therapy after cataract surgery can prevent the aggravation of diabetic macular edema in DR patients. However, due to the lack of systematic review of the clinical effect of anti-VEGF drugs in DR patients undergoing cataract surgery, the use of anti-VEGF drugs is relatively conservative in clinic. It is believed that with the deepening of research and the progress of clinical trials, the wide application of anti-VEGF drugs in clinical practice is expected to provide more accurate and effective treatment for DR patients in the future.
The chemical extraction method was used to prepare the rat uterine decellularized scaffolds, and to investigate the feasibility of preparing the extracellular matrix (ECM) hydrogel. The rat uterus were collected and extracted by 1%sodium dodecyl sulfate (SDS), 3% TritonX-100 and 4% sodium deoxycholate (SDC) in sequence. Scanning electron microscopy, histochemical staining and immunohistochemistry was used to assess the degree of decellularization of rat uterine scaffold. The prepared decellularized scaffold was digested with pepsin to obtain a uterine ECM hydrogel, and the protein content of ECM was determined by specific ELISA kit. Meanwhile, the mechanical characteristic of ECM hydrogel was measured. The results showed that the chemical extraction method can effectively remove the cells effectively in the rat uterine decellularized scaffold, with the ECM composition preserved completely. ECM hydrogel contains a large amount of ECM protein and shows a good stability, which provides a suitable supporting material for the reconstruction of endometrium in vitro.
Medical image fusion realizes advantage integration of functional images and anatomical images. This article discusses the research progress of multi-model medical image fusion at feature level. We firstly describe the principle of medical image fusion at feature level. Then we analyze and summarize fuzzy sets, rough sets, D-S evidence theory, artificial neural network, principal component analysis and other fusion methods' applications in medical image fusion and get summery. Lastly, we in this article indicate present problems and the research direction of multi-model medical images in the future.
Sample size, mean and standard deviation are necessary when conducting meta-analysis for continuous outcomes. Advanced methods of data extraction were needed if the mean and the standard deviation couldn’t be obtained from a literature directly. Eight methods were introduced and two examples were given to illustrate how to apply the methods.
The brain computer interface (BCI) can be used to control external devices directly through electroencephalogram (EEG) information. A multi-linear principal component analysis (MPCA) framework was used for the limitations of tensor form of multichannel EEG signals processing based on traditional principal component analysis (PCA) and two-dimensional principal component analysis (2DPCA). Based on MPCA, we used the projection of tensor-matrix to achieve the goal of dimensionality reduction and features exaction. Then we used the Fisher linear classifier to classify the features. Furthermore, we used this novel method on the BCI competitionⅡdataset 4 and BCI competitionⅣdataset 3 in the experiment. The second-order tensor representation of time-space EEG data and the third-order tensor representation of time-space-frequency EEG data were used. The best results that were superior to those from other dimensionality reduction methods were obtained by much debugging on parameter P and testQ. For two-order tensor, the highest accuracy rates could be achieved as 81.0% and 40.1%, and for three-order tensor, the highest accuracy rates were 76.0% and 43.5%, respectively.
Inferior myocardial infarction is an acute ischemic heart disease with high mortality, which is easy to induce life-threatening complications such as arrhythmia, heart failure and cardiogenic shock. Therefore, it is of great clinical value to carry out accurate and efficient early diagnosis of inferior myocardial infarction. Electrocardiogram is the most sensitive means for early diagnosis of inferior myocardial infarction. This paper proposes a method for detecting inferior myocardial infarction based on densely connected convolutional neural network. The method uses the original electrocardiogram (ECG) signals of serially connected Ⅱ, Ⅲ and aVF leads as the input of the model and extracts the robust features of the ECG signals by using the scale invariance of the convolutional layers. The characteristic transmission of ECG signals is enhanced by the dense connectivity between different layers, so that the network can automatically learn the effective features with strong robustness and high recognition, so as to achieve accurate detection of inferior myocardial infarction. The Physikalisch Technische Bundesanstalt diagnosis public ECG database was used for verification. The accuracy, sensitivity and specificity of the model reached 99.95%, 100% and 99.90%, respectively. The accuracy, sensitivity and specificity of the model are also over 99% even though the noise exists. Based on the results of this study, it is expected that the method can be introduced in the clinical environment to help doctors quickly diagnose inferior myocardial infarction in the future.
Automatic classification of different types of cough plays an important role in clinical. In the previous research of cough classification or cough recognition, traditional Mel frequency cepstrum coefficients (MFCC) which extracts feature mainly from low frequency band is usually used as feature expression. In this paper, by analyzing the distributions of spectral energy of dry/wet cough, it is found that spectral difference of two types of cough exits mainly in middle frequency band and high frequency band. To better reflect the spectral difference of dry cough and wet cough, an improved method of extracting reverse MFCC is proposed. In this method, reverse Mel filter-bank in which filters are allocated in reverse Mel scale is adopted and is improved by placing filters only in the frequency band with high spectral energy. As a result, features are mainly extracted from the frequency band where two types of cough show both high spectral energy and distinguished difference. Detailed process of accessing improved reverse MFCC was introduced and hidden Markov models trained by 60 dry cough and 60 wet cough were used as cough classification model. Classification experiment results for 120 dry cough and 85 wet cough showed that, compared to traditional MFCC, better classification performance was achieved by the proposed method and the total classification accuracy was raised from 89.76% to 93.66%.