Computer-aided algorithm plays an important role in disease diagnosis through medical images. As one of the major cancers, lung cancer is commonly detected by computer tomography. To increase the survival rate of lung cancer patients, an early-stage diagnosis is necessary. In this paper, we propose a new structure, multi-level cross residual convolutional neural network (ML-xResNet), to classify the different types of lung nodule malignancies. ML-xResNet is constructed by three-level parallel ResNets with different convolution kernel sizes to extract multi-scale features of the inputs. Moreover, the residuals are connected not only with the current level but also with other levels in a crossover manner. To illustrate the performance of ML-xResNet, we apply the model to process ternary classification (benign, indeterminate, and malignant lung nodules) and binary classification (benign and malignant lung nodules) of lung nodules, respectively. Based on the experiment results, the proposed ML-xResNet achieves the best results of 85.88% accuracy for ternary classification and 92.19% accuracy for binary classification, without any additional handcrafted preprocessing algorithm.

Left ventricular ejection fraction (LVEF) is a critical indicator for assessing cardiac function and diagnosing heart disease. LVEF can be derived by estimating the left ventricular volume from end-systolic and end-diastolic frames through echocardiography segmentation. However, current algorithms either focus primarily on single-frame segmentation, neglecting the temporal and spatial correlations between consecutive frames, or often fail to effectively address the inherent challenges posed by the low-contrast and fuzzy edges characteristic of echocardiography, thereby resulting in suboptimal segmentation outcomes. In this study, we propose a novel self-attention-based dual-task learning approach for automatic left ventricle segmentation. First, we introduce a multi-scale edge-attention U-Net to achieve supervised semantic segmentation of echocardiography. Second, an optical flow network is developed to capture the changes in the optical flow fields between frames in an unsupervised manner. These two tasks are then jointly trained using a temporal consistency mechanism to extract spatio-temporal features across frames. Experimental results demonstrate that our model outperforms existing segmentation methods. Our proposed method not only enhances the performance of semantic segmentation but also improves the consistency of segmentation between consecutive frames.

Infrared small target detection is crucial in military reconnaissance, remote sensing, and so on. However, due to its small size and the high coupling with complex backgrounds, the present methods still face challenges in precise detection. They predominantly focus on target feature learning while neglecting the critical role of background modeling for small target decoupling. To this end, we propose a dual-task collaborative optimization network (DCONet), which decouples the task into background estimation and target segmentation using a multistage iterative optimization strategy. First, considering significant directional distribution characteristics in infrared backgrounds, we propose a direction-aware background estimation module (DBEM) to capture directional features, such as clouds and trees, thereby generating an initial background estimation. Second, we propose a background suppression gating unit (BSGU), which employs a gating mechanism and a channel-level adjustment factor to dynamically suppress background noise based on the preliminary background estimation, thereby generating the target segmentation result. Finally, the estimated background, target segmentation, and the reconstructed original image based on them are propagated to the next stage for further iterative optimization. The experimental results show that DCONet performs better than existing methods across three public datasets. The source code is available at https://github.com/tustAilab/DCONet