Compared to traditional Computed Tomography (CT) scans and floatation catheters, chest X-ray offers an efficient, safe and timely examination paradigm, with broader range of scenarios (including intensive care units), for the detection of Pulmonary Arterial Hypertension (PAH). However, it is difficult to learn the variable radiological features of PAH from X-rays due to its low resolution and low contrast. To address the above issues, we propose a diversified features learning framework to fully explore the PAH-related representation from chest X-ray. We first employ a Chest Feature Enhancement Attention (CFEA) module to enhance the initial feature representation. Then, we employ the Deep Temporal Anti-Interference Metric Learning (TAIML) module to fully explore the PAH-related features. We incorporate the information on the temporal evolution of patients’ conditions. Specifically, a patient x , after undergoing treatment, may exhibit two possible states: x + (ill) and x − (cured). Therefore, we can define the distance d ( x , x + ) as the intra-class structural distance, and the distance d ( x , x − ) as the inter-class safe distance. Unlike existing metric learning, we adopt a new strategy: we push positive samples towards negative samples, but ensure distance between them is no less than d ( x , x − ) , thereby enhancing intra-class diversity while maintaining discriminability. Meanwhile, we ensure that the distance between positive samples is greater than d ( x , x + ) , thereby preserving the intra-class structure. Through these two steps, we can learn a diversified but discriminative representation of PAH. Comprehensive experiments showed the our model achieved an impressive accuracy of 86.27 % and an AUC of 0.857 in identifying PAH patients. The code is available at https://github.com/zgfdmn/PAH .

Cross-domain label propagation (LP) faces two main challenges: 1) learning domain-invariant and 2) discriminative feature representations and obtaining high-confidence predicted labels. The distribution differences between domains can make labels difficult to propagate across domains. Low-quality labels can distort the modeling process associated with label-induced loss, resulting in decreased performance. We propose a novel cross-domain image classification method, namely, robust LP and graph embedding (RLPGE). We introduce a nuclear norm maximization constraint in order to make the predicted labels more diverse in categories while preserving their discriminability. The graph embedding process brings two nearby same-class samples close in the embedding subspace, ensuring domain invariance and local discriminability of the embedded features. For optimal graph learning, we simultaneously optimize the cross-domain graph and two intradomain graphs using both features and labels, enhancing their local discriminability and robustness to feature noise. We conducted comprehensive experiments on four cross-domain image classification datasets. The results demonstrate that our proposed RLPGE method outperforming some state-of-the-art approaches

In this paper, we develop a progressive local and non-local interactive network with multi-scale cross-content deeply discriminative learning to solve image deraining. The proposed model contains two key techniques: 1) Progressive Local and Non-Local Interactive Network (PLNLIN) and 2) Multi-Scale Cross-Content Deeply Discriminative Learning (MCDDL). The PLNLIN is a U-shaped encoder-decoder network, where the proposed new Progressive Local and Non-Local Interactive Module (PLNLIM) is the basic unit in the encoder-decoder framework. The PLNLIM fully explores local and non-local learning in convolution and Transformer operation respectively and the local and non-local content are further interactively learned in a progressive manner. The proposed MCDDL not only discriminates the output of the generator but also receives the deep content from the generator to distinguish real and fake features at each side layer of the discriminator in a multi-scale manner. We show that the proposed MCDDL has fast and stable convergence properties that lack in existing discriminative learning manners. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art methods on five public synthetic datasets and one real-world data. The source codes will be made available at https://github.com/supersupercong/PLNLIN-MCDDL.

While current CNN-based low-light image enhancement (LIE) approaches have achieved significant progress, they often fail to generate better perceptual quality which requires restoring better details and more natural colors. To address these problems, we set a new path, called PercepLIE, by presenting the VQGAN with Multi-luminance Detail Compensation (MDC) and Global Color Adjustment (GCA). Specifically, observed that latent light features of the low-light images are quite different from those captured in normal light, we utilize VQGAN to explore the latent light representation of normal-light images to help the estimation of the low-light and normal-light mapping. Furthermore, we employ Gamma correction with varying Gamma values on the gradient to create multi-luminance details, forming the basis for our MDC module to facilitate better detail estimation. To optimize the colors of low-light input images, we introduce a simple yet effective GCA module that is based on spatially-varying representation between the estimated normal-light images in this module and low-light inputs. By combining the VQGAN with MDC and GCA within a stage-wise training mechanism, our method generates images with finer details and natural colors and achieves favorable performance on both synthetic and real-world datasets in terms of perceptual quality metrics including NIQE, PI, and LPIPS. The source codes will be made available at https://github.com/supersupercong/PercepLIE.

Multi-dimensional analysis in echocardiography has attracted attention due to its potential for clinical indices quantification and computer-aided diagnosis. It can utilize various information to provide the estimation of multiple cardiac indices. However, it still has the challenge of inter-task conflict. This is owing to regional confusion, global abnormalities, and time-accumulated errors. Task mapping methods have the potential to address inter-task conflict. However, they may overlook the inherent differences between tasks, especially for multi-level tasks (e.g., pixel-level, image-level, and sequence-level tasks). This may lead to inappropriate local and spurious task constraints. We propose cross-space consistency (CSC) to overcome the challenge. The CSC embeds multi-level tasks to the same-level to reduce inherent task differences. This allows multi-level task features to be consistent in a unified latent space. The latent space extracts task-common features and constrains the distance in these features. This constrains the task weight region that satisfies multiple task conditions. Extensive experiments compare the CSC with fifteen state-of-the-art echocardiographic analysis methods on five datasets (10,908 patients). The result shows that the CSC can provide left ventricular (LV) segmentation, (DSC = 0.932), keypoint detection (MAE = 3.06mm), and keyframe identification (accuracy = 0.943). These results demonstrate that our method can provide a multi-dimensional analysis of cardiac function and is robust in large-scale datasets.

The estimation of multitype cardiac indices from cardiac magnetic resonance imaging (MRI) and computed tomography (CT) images attracts great attention because of its clinical potential for comprehensive function assessment. However, the most exiting model can only work in one imaging modality (MRI or CT) without transferable capability. In this article, we propose the multitask learning method with the reverse inferring for estimating multitype cardiac indices in MRI and CT. Different from the existing forward inferring methods, our method builds a reverse mapping network that maps the multitype cardiac indices to cardiac images. The task dependencies are then learned and shared to multitask learning networks using an adversarial training approach. Finally, we transfer the parameters learned from MRI to CT. A series of experiments were conducted in which we first optimized the performance of our framework via ten-fold cross-validation of over 2900 cardiac MRI images. Then, the fine-tuned network was run on an independent data set with 2360 cardiac CT images. The results of all the experiments conducted on the proposed adversarial reverse mapping show excellent performance in estimating multitype cardiac indices.