Aerial object detection, particularly in Unmanned Aerial Vehicle (UAV) scenarios, presents a persistent challenge due to the prevalence of small targets with low-resolution appearances, ambiguous features, and high missed-detection rates. Although existing real-time detectors have made significant progress, achieving an optimal balance between detection accuracy, robustness, and computational efficiency across diverse aerial environments remains difficult. To address these limitations, we propose SPNC-YOLO, a real-time aerial detection framework built on an architecture-optimized version of YOLOv11. This framework integrates three computationally efficient and complementary innovations designed to resolve specific bottlenecks in small object detection. First, we introduce an improved Space-to-Depth Convolution (SPD-Conv) to mitigate the loss of fine-grained spatial information typically caused by strided convolutions, thereby enhancing feature preservation in deeper layers. Second, a dedicated high-resolution P2 detection layer is incorporated to enlarge the receptive field for distant and minute objects, significantly improving cross-scale feature modeling. Third, we reconstruct the C3K2 block by integrating a Selective Boundary-Aware (SBA) unit and replacing standard bottleneck convolutions with Partial Convolutions (PCConv). Extensive experiments on three benchmark aerial datasets—VisDrone2019, UAVDT, and AI-TOD—demonstrate that SPNC-YOLO achieves superior performance compared to the baseline YOLOv11 and other state-of-the-art real-time detectors.
Deep learning has achieved substantial progress in breast cancer detection from mammograms, with recent state-of-the-art methods leveraging multiple views, such as craniocaudal (CC) and mediolateral oblique (MLO), to improve diagnostic accuracy. Most existing approaches pursue performance gains by increasing architectural complexity and rely exclusively on image data. In contrast, clinical practice heavily depends on expert-derived semantic descriptors defined by the Breast Imaging Reporting and Data System (BI-RADS) lexicon, which guide the interpretation of radiologists. This discrepancy raises a fundamental question regarding the relationship between explicit radiological domain knowledge and learned deep visual representations in breast cancer detection. In this work, we systematically investigate the impact of integrating BI-RADS descriptors into deep learning models for mammography-based breast cancer detection. We evaluate dedicated architectures designed to fuse BI-RADS descriptors with image features and compare them against image-only models and BI-RADS–only tree-based baselines. Our results show that incorporating BI-RADS descriptors consistently improves performance across lesion types and evaluation metrics. Notably, relatively simple models augmented with BI-RADS descriptors outperform substantially more complex image-only architectures. Moreover, BI-RADS integration enhances generalization, enabling a single unified model to effectively classify both mass and calcification lesions. These findings demonstrate that combining structured expert knowledge with deep learning yields more accurate, robust, and clinically aligned breast cancer detection systems.
Vehicle re-identification (ReID) under extreme day-night illumination variations poses substantial challenges for intelligent transportation systems. Nighttime images exhibit reduced contrast, headlight glare, and illumination-induced appearance shifts, all of which degrade cross-domain matching. Existing approaches operate predominantly in spatial domains, where appearance features remain coupled to illumination-dependent statistics. Furthermore, frequency-domain components can encode structural cues that are less sensitive to illumination drift, yet current methods lack explicit quantification of uncertainty to assess feature reliability under challenging conditions. We propose Hierarchical Frequency-Aware Cross-Modal Transformer (HFC-Transformer) for day -night vehicle ReID, which extracts illumination-robust structural representations through frequency-domain analysis and uncertainty-guided fusion. The framework combines modality-specific convolutional feature extraction within a shared Transformer encoding setup with frequency-domain structural disentanglement to perform angular-frequency decomposition. This allows us to filter illumination-sensitive interactions and enforces orthogonal alignment for paired day-night observations. An uncertainty-guided fusion module is then used to aggregate the refined frequency components. Comprehensive experiments on DN-Wild and DN-348 day-night benchmarks validate the effectiveness of HFC-Transformer, achieving 73.6
Automatic detection of Coronary Artery Disease (CAD) based on Cardiac Magnetic Resonance Imaging (CMRI) remains challenging due to the severe scarcity of publicly available CAD-positive scan samples. High acquisition costs, various imaging constraints, safety concerns, etc. are the main reasons for the limited availability of CAD-positive CMRI samples. However, CMRI samples with no sign of CAD are available in somewhat adequate numbers. Such a class imbalance of available training samples causes difficulties in training deep learning architectures for robust detection of the disease. Here, we introduce CAD-GAN+, a pathology-aware image data augmentation framework capable of generating synthetic CMRI samples from randomly sampled Gaussian latent vectors. The two-stage pipeline of the proposed framework includes a GAN with frequency-domain regularization. It is trained with morphological pathology priors to produce CMRI slices consistent with CAD exhibiting anatomically plausible disease characteristics. The classifier (ResNet-34 with Squeeze-and-Excitation attention) of the second stage is trained with an entropy-weighted loss and uses a composite CAD-Consistency Score to identify generated samples that are implausible or diagnostically irrelevant. Only high-fidelity samples validated by the classifier are used to augment the training set. Extensive experiments involving a ResNet-34 based baseline classifier trained only on real samples, as well as the same trained using various augmented training sets, show that the proposed CAD-GAN+ framework substantially improves CAD detection performance, producing improvements of up to 20
Single-image dehazing remains challenging due to the spatially global nature of atmospheric scattering, which degrades contrast and suppresses fine structural details. While recent convolutional and transformer-based methods have improved dehazing performance, many approaches operate purely in the spatial domain and implicitly entangle haze-related degradation with structural information, making it challenging to preserve fine details under moderate and dense haze. We propose a frequency-aware multi-scale convolution–transformer network motivated by the observation that haze predominantly affects low-frequency components, whereas edges and textures reside in high-frequency components. Our model separates haze-dominated low-frequency features and structure-rich high-frequency details using a fixed, separable low-pass filtering module derived from CDF 9/7 analysis taps, with the high-frequency component obtained as a residual. Integrated into a coarse-to-fine U-Net encoder–decoder with hybrid convolution–transformer blocks, deterministic high-frequency injection during decoding preserves details while enabling effective global haze removal. Experiments on natural and remote sensing benchmark datasets (NID, RSID, and SateHaze1k) show that the proposed method achieves the best performance among the compared approaches while using significantly fewer parameters.
