Retinopathy of Prematurity (ROP) is a retinal vasproliferative disorder disease principally observed in infants born prematurely with low birth weight. ROP is an important cause of childhood blindness. Although automatic or semi-automatic diagnosis of ROP has been conducted, most previous studies have focused on “plus” disease, which is indicated by abnormalities of retinal vasculature. Few studies have reported methods for identifying the “stage” of the ROP disease. Deep neural networks have achieved impressive results in many computer vision and medical image analysis problems, raising expectations that it might be a promising tool in the automatic diagnosis of ROP. In this paper, convolutional neural networks with a novel architecture are proposed to recognize the existence and severity of ROP disease per-examination. The severity of ROP is divided into mild and severe cases according to the disease progression. The proposed architecture consists of two sub-networks connected by a feature aggregate operator. The first sub-network is designed to extract high-level features from images of the fundus. These features from different images in an examination are fused by the aggregate operator, then used as the input for the second sub-network to predict its class. A large data set imaged by RetCam 3 is used to train and evaluate the model. The high classification accuracy in the experiment demonstrates the effectiveness of the proposed architecture for recognizing the ROP disease.

The emerging ultra-wide field of view (UWF) fundus color imaging is a powerful tool for fundus screening. However, manual screening is labor-intensive and subjective. Based on 2644 UWF images, a set of early fundus abnormal screening system named DeepUWF is developed. DeepUWF includes an abnormal fundus screening subsystem and a disease diagnosis subsystem for three kinds of fundus diseases (retinal tear & retinal detachment, diabetic retinopathy and pathological myopia). The components in the system are composed of a set of excellent convolutional neural networks and two custom classifiers. However, the contrast of UWF images used in the research is low, which seriously limits the extraction of fine features of UWF images by depth model. Therefore, the high specificity and low sensitivity of prediction results have always been difficult problems in research. In order to solve this problem, six kinds of image preprocessing techniques are adopted, and their effects on the prediction performance of fundus abnormal and three kinds of fundus diseases models are studied. A variety of experimental indicators are used to evaluate the algorithms for validity and reliability. The experimental results show that these preprocessing methods are helpful to improve the learning ability of the networks and achieve good sensitivity and specificity. Without ophthalmologists, DeepUWF has potential application value, which is helpful for fundus health screening and workflow improvement.

Ultrasound based estimation of fetal biometry is extensively used to diagnose prenatal abnormalities and to monitor fetal growth, for which accurate segmentation of the fetal anatomy is a crucial prerequisite. Although deep neural network-based models have achieved encouraging results on this task, inevitable distribution shifts in ultrasound images can still result in severe performance drop in real world deployment scenarios. In this paper, we propose a complete ultrasound fetal examination system to deal with this troublesome problem by repairing and screening the anatomically implausible results. Our system consists of three main components: a routine segmentation network, a fetal anatomical key points guided repair network, and a shape-coding based selective screener. Guided by the anatomical key points, our repair network has stronger cross-domain repair capabilities, which can substantially improve the outputs of the segmentation network. By quantifying the distance between an arbitrary segmentation mask to its corresponding anatomical shape class, the proposed shape-coding based selective screener can then effectively reject the entire implausible results that cannot be fully repaired. Extensive experiments demonstrate that our proposed framework has strong anatomical guarantee and outperforms other methods in three different cross-domain scenarios. We release source codes and datasets on our GitHub: https://github.com/gaojun0821/RepairNet .

Crowd counting is one of the important and challenging research topics in computer vision. In recent years, with the rapid development of deep learning, the model architectures, learning paradigms, and counting accuracy have undergone significant changes. To help researchers quickly understand the research progress in this area, this paper presents a comprehensive survey of crowd density estimation and counting approaches. Initially, the technical challenges and commonly used datasets are intoroduced for crowd counting. Crowd counting approaches is them categorized into two groups based on the feature extraction methods employed: traditional approaches and deep learning‐based approaches. A systematic and focused analysis of deep learning‐based approaches is proposed. Subsequently, some training and evaluation details are introduced, including labels generation, loss functions, supervised training methods, and evaluation metrics. The accuracy and robustness of selected classical models are further compared. Finally, future prospects, strategies, and challenges are discussed for crowd counting. This review is comprehensive and timely, stemming from the selection of prominent and unique works.