Representation learning offers a conduit to elucidate distinctive features within the latent space and interpret the deep models. However, the randomness of lesion distribution and the complexity of low-quality factors in medical images pose great ...

Medical images captured in less-than-optimal conditions may suffer from quality degradation, such as blur, artifacts, and low lighting, which potentially leads to misdiagnosis. Unfortunately, state-of-the-art medical image enhancement methods face ...

The existing barely-supervised medical image segmentation (BSS) methods, adopting a registration-segmentation paradigm, aim to learn from data with very few annotations to mitigate the extreme label scarcity problem. However, this paradigm poses a ...

State-of-the-art knowledge distillation (KD) methods aim to capture the underlying information within the teacher and explore effective strategies for knowledge transfer. However, due to challenges such as blurriness, noise, and low contrast ...

3D medical image segmentation is critical for clinical diagnosis and treatment planning. Recently, with the powerful generalization, the foundational segmentation model SAM is widely used in medical images. However, the existing SAM variants still ...

The dynamic variation in the spatio-temporal organizational patterns of brain functional modules (BFMs) associated with brain disorders remains unclear. To solve this issue, we propose an end-to-end transformer-based framework for sufficiently ...

Current state-of-the-art medical image segmentation techniques predominantly employ the encoder–decoder architecture. Despite its widespread use, this U-shaped framework exhibits limitations in effectively capturing multi-scale features through ...

The composite current source (CCS) model has been adopted as an advanced timing model that represents the current behavior of cells for improved accuracy and better capability than traditional non-linear delay models (NLDM) to model complex dynamic ...

• A novel strategy to generate the mesoscale asphalt concrete model was proposed.
• Aggregate morphology was quantified by form scaling and spherical harmonic modeling.
• PCA was employed to reproduce aggregates with similar morphology ...

Establishing a mesoscale model of asphalt concrete is significantly challenging due to its inherent heterogeneity and high proportion of aggregates. Initially, a novel approach for systematically quantifying aggregate morphology by integrating ...

\beginabstract Dynamic graphs play an important role in many fields like social relationship analysis, recommender systems and medical science, as graphs evolve over time. It is fundamental to capture the evolution patterns for dynamic graphs. Existing ...

Multi-label Chest X-ray (CXR) images often contain rich label relationship information, which is beneficial to improve classification performance. However, because of the intricate relationships among labels, most ...

• An end-to-end multi-label learning framework is proposed for multi-label CXR image classification.
• Consistency loss between global and local label correlations help learns more accurate label correlation.
• Label correlation guided ...

This paper presents a hierarchical tactical merging optimization (HTMO) approach for connected and automated vehicles (CAV) at freeway merging segments. The proposed approach comprises two layers: a merging sequencing layer and a trajectory optimization ...

Accurate and efficient pre-routing timing estimation is particularly crucial in timing-driven placement, as design iterations caused by timing divergence are time-consuming. However, existing machine learning prediction models overlook the impact of ...

Statistical timing characterization for standard cell library poses significant challenge to accuracy and runtime cost. Prior analytical and machine learning-based methods neglect the profound influence induced by layout-dependent parasitic resistor and ...

In this paper, we propose a novel design of a one-hot transmission-gate multiplexer (OTG-MUX), which combines Complementary Metal Oxide Semiconductor (CMOS) logic with Transmission Gate (TG) logic. We employ four techniques to enhance the ...

• A TG-MUX2 with only 8 transistors is proposed as a compact basic cell for MUX.
• The combination of TG-MUX2 and one-hot signal improves transistor utilization & gain.
• “Wired-AND” technology is introduced to reduce cascaded cells and ...

The Belt and Road (B&R) plan is put out within the framework of global economics and strategic growth. This study examines the written material of popular tourist sites along B&R and the tourism assets from the viewpoint of B&R, based on the wireless ...

Retinal fundus images have been widely utilized for screening Diabetic Retinopathy (DR). The lesion information contained in these images is indispensable for the diagnosis of DR. The acquisition of lesion information depends on the sophisticated ...

Class imbalance problem commonly exists in multi-label classification (MLC) tasks. It has non-negligible impacts on the classifier performance and has drawn extensive attention in recent years. Borderline oversampling has been widely used in ...

• A new borderline oversampling technique for multi-label imbalanced learning.
• Defining self-borderline and cross-borderline samples in multi-label data sets.
• Handling one-vs-rest imbalance in multi-label imbalanced learning.
• ...

Deep regression models typically learn in an end-to-end fashion without explicitly emphasizing a regression-aware representation. Consequently, the learned representations exhibit fragmentation and fail to capture the continuous nature of sample orders, ...

Multi-label data classification has received much attention due to its wide range of application domains. Unfortunately, a class imbalance problem often occurs in multi-label datasets, causing challenges for classification algorithms. ...