Several developments in computational image processing methods assist the radiologist in detecting abnormal breast tissue in recent years. Consequently, deep learning-based models have become crucial for early screening and interpretation of mammographic images for breast masses diagnosis, helping for successful treatment. Breast masses and calcification is an essential parameter for the prognosis of breast cancer. However, the mammographic image’s mass detection needs a deeper investigation due to the breast masses’ heterogeneity and anomalies’ characteristics that are easily confused with other objects present in the image. Hence, this study proposed a deep learning-based convolutional neural network (ConvNet) that will incorporate both mammography and clinical variables to predict and classify breast masses to assist the expert’s decision-making processes. We trained our proposed model with 322 scanned digital mammographic images of the MIAS (Mammogram Image Analysis Society) dataset and 580 images of the private dataset to evaluate the performance, which is highly imbalanced. This study aimed to perform an automatic and comprehensive characterization of breast masses using appropriate layers deep ConvNet model with high accuracy true-positive rate, decreased error rate and applying data-augmentation techniques. We obtained a classification accuracy of 97% applying the filtered deep features, which is the best performance from the existing approaches.

The wide adoption of electronic medical record (EMR) systems causes rapid growth of medical and clinical data. It makes the medical named entity recognition (NER) technologies become critical to find useful patient information in the medical dataset. However, the medical terminologies usually have the characteristics of inherent complexity and ambiguity, it is difficult to capture context-dependency representations by supervision signal from a simple single layer structure model. In order to address this problem, this paper proposes a hybrid model based on stacked Bidirectional Long Short-Term Memory (BILSTM) for medical named entity recognition, which we call BSBC (BERT combined with stacked BILSTM and CRF). First, we use Bidirectional Encoder Representation from Transformers (BERT) to perform unsupervised learning on an unlabeled dataset to obtain character-level embeddings. Then, stacked BILSTM is utilized to obtain context-dependency representations through the multi hidden layers structure. Finally, Conditional Random Field (CRF) is used to predict sequence tags. The experiment results show that our method significantly outperforms the baseline methods, it serves as a strong alternative approach compared with traditional methods.

Hydronephrosis may lead to many potential diseases, and the diagnosis of hydronephrosis is time-consuming and laborious. To assist physicians in hydronephrosis diagnosis and treatment planning, an accurate and automatic kidney segmentation method is highly required in clinical practice. In recent years, deep convolutional neural networks such as Unet plays a key role in the field of image segmentation, but Unet itself cannot adjust the receptive field actively, which may result in poor attention to the characteristics of the segmented target. We propose an encoder-decoder network with weighted skip connections and the idea of hierarchical equal resolution that can manually control the receptive field. We evaluated our method by comparing it with various classical networks using a dataset of 1850 annotated images. The MPA of the model is 94.12 and the MIoU is 89.49, which outperformed other classical networks we compared to.

Hydronephrosis is a common renal disease in children which can lead to a series of complications, and ultrasonography is a basic examination usually performed on suspected hydronephrosis patients. If we can use deep learning approaches to judge and grade the disease in the ultrasonic examination stage, we can save a lot of manpower, medical resources, money, and help the suffered patients. For the semantic segmentation of kidney ultrasound image, we designed an Attention-based Pyramid Scene Parsing Network (A-PSPNet), the core of which is the basic feature extraction network combining Convolutional Block Attention Module (CBAM) and pyramid analysis module. Experiments were carried out on a hydronephrosis dataset containing 1850 annotated ultrasound images, including the arrangement of attention units, statistical computing power, and comparison of the effectiveness between the benchmark and our proposed method. Our constructed model achieved better segmentation performance than benchmarks with only little extra overhead, which validated the lightweight and effectiveness of the model.