Leukemia diagnosis based on bone marrow cell morphology primarily relies on the manual microscopy of bone marrow smears. However, this method is greatly affected by subjective factors and tends to lead to misdiagnosis. This study proposes using bone marrow cell microscopy images and employs convolutional neural network (CNN) combined with transfer learning to establish an objective, rapid, and accurate method for classification and diagnosis of LKA (AML, ALL, and CML). We collected cell microscopy images of 104 bone marrow smears (including 18 healthy subjects, 53 AML patients, 23 ALL patients, and 18 CML patients). The perfect reflection algorithm and a self-adaptive filter algorithm were first used for preprocessing of bone marrow cell images collected from experiments. Subsequently, 3 CNN frameworks (Inception-V3, ResNet50, and DenseNet121) were used to construct classification models for the raw dataset and preprocessed dataset. Transfer learning was used to improve the prediction accuracy of the model. Results showed that the DenseNet121 model based on the preprocessed dataset provided the best classification results, with a prediction accuracy of 74.8%. The prediction accuracy of the DenseNet121 model that was obtained by transfer learning optimization was 95.3%, which was increased by 20.5%. In this model, the prediction accuracies of the normal groups, AML, ALL, and CML were 90%, 99%, 97%, and 95%, respectively. The results showed that the leukemic cell morphology classification and diagnosis based on CNN combined with transfer learning is feasible. Compared with conventional manual microscopy, this method is more rapid, accurate, and objective.
Recently, approaches based on deep learning are quite prevalent in the area of remote sensing scene classification. Though significant success has been achieved, these approaches are still subject to an excess of parameters and extremely dependent on a large quantity of labeled data. In this study, few-shot learning is used for remote sensing scene classification tasks. The goal of few-shot learning is to recognize unseen scene categories given extremely limited labeled samples. For this purpose, a novel task-adaptive embedding network is proposed to facilitate few-shot scene classification of remote sensing images, referred to as TAE-Net. A feature encoder is first trained on the base set to learn embedding features of input images in the pre-training phase. Then in the meta-training phase, a new task-adaptive attention module is designed to yield the task-specific attention, which can adaptively select informative embedding features among the whole task. In the end, in the meta-testing phase, the query image derived from the novel set is predicted by the meta-trained model with limited support images. Extensive experiments are carried out on three public remote sensing scene datasets: UC Merced, WHU-RS19, and NWPU-RESISC45. The experimental results illustrate that our proposed TAE-Net achieves new state-of-the-art performance for few-shot remote sensing scene classification.
Scene classification is a critical technology to solve the challenges of image search and image recognition. It has become an indispensable and challenging research topic in the field of remote sensing. At present, most scene classifications are solved by deep neural networks. However, existing methods require large-scale training samples and are not suitable for actual scenarios with only a few samples. For this reason, a framework based on metric learning and local descriptors (MLLD) is proposed to enhance the classification effect of remote sensing scenes on the basis of few-shot. Specifically, MLLD adopts task-level training that is carried out through meta-learning, and meta-knowledge is learned to improve the model’s ability to recognize different categories. Moreover, Manifold Mixup is introduced by MLLD as a feature processor for the hidden layer of deep neural networks to increase the low confidence space for smoother decision boundaries and simpler hidden layer representations. In the end, a learnable metric is introduced; the nearest category of the image is matched by measuring the similarity of local descriptors. Experiments are conducted on three public datasets: UC Merced, WHU-RS19, and NWPU-RESISC45. Experimental results show that the proposed scene classification method can achieve the most advanced results on limited datasets.
Background:In-depth research on tumors has shown that cancer stem cells (CSCs) play a crucial role in tumorigenesis. However, the mechanisms underlying the growth and maintenance of CSCs in stomach adenocarcinoma (STAD) are unclear. This study sought to investigate the expression of stem cell-related genes in STAD. Methods:We identified key genes related to STAD stem cell characteristics by combining gene expression data obtained from The Cancer Genome Atlas to define a messenger ribonucleic acid expression-based stemness index (mRNAsi) based on mRNA expression. The correlations between the mRNAsi and STAD clinical characteristics, including age, tumor grade, pathological stage, and survival status, were explored.Additionally, a weighted gene co-expression network analysis was conducted to identify relevant modules and key genes. The expression verification and functional analysis of the key genes was carried out using multiple databases, including the TIMER (https://cistrome.shinyapps.io/timer/), and Gene Expression Profiling Integrative Analysis, and Gene Expression Omnibus databases. Results:The mRNAsi score was closely related to the clinical characteristics of STAD, including age, tumor grade, pathological stage, and survival status. Similarly, the mRNAsi score was significantly higher in STAD tissues than normal tissues, and the score decreased with tumor stage. The higher the mRNAsi score, the higher the overall survival rate. We screened a module of interest and found a strong correlation between 19 key genes. Among these 19 key genes, 16 had previously been shown to be closely related to STAD survival. The functional analysis showed that these key genes were linked to cell-cycle events, such as chromosome separation, mitosis, and microtubule movement.Conclusions: We identified 19 key genes that play an important role in the maintenance of STAD stem cells. Among these genes, 16 play a role in predicting the prognosis of STAD patients. The cell-cycle pathway was the most important signaling pathway for the key genes associated with STAD stem cells. These findings may provide a new rationale for screening therapeutic targets and the characterization of STAD stem cells.
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