AI-driven disease diagnosis relies on large datasets and advanced algorithms, but data scarcity poses challenges for infectious disease detection. To address this, we propose the direct-self-attention Wasserstein generative adversarial network (DSAWGAN), which enhances diagnostic accuracy with limited data. By integrating attention mechanisms and Wasserstein distance optimization, DSAWGAN improves convergence speed, stability, and image quality. We compared it with traditional data augmentation methods using classification neural networks. The trained models were then deployed in a mobile app for rapid, portable, and cost-effective diagnostics. With only half the original data (n = 1500), accuracy increased from 98.00% to 99.33%. Even with just 10% (n = 300), the model maintained 92.67% accuracy, demonstrating its effectiveness in resource-limited settings.
On-chip hyperspectral imaging (HSI) captures substantially richer spectral signatures than conventional RGB sensors, thereby enhancing semantic segmentation performance for autonomous driving and intelligent transportation systems. However, the elevated dimensionality of HSI data poses considerable computational burdens, necessitating effective compression. We address this challenge by introducing MobileSACNet, a MobileUNet-based framework equipped with a Spectral Attention Compressor (SAC). This nonlinear compression module integrates multi-stage 3D convolutions and dynamic channel attention through two complementary branches: (1) a spectral compression branch employing depthwise separable 3D layers with squeeze-excitation attention to refine spectral representations, and (2) a residual pathway that preserves spatial-spectral correlations via identity mappings to mitigate information loss. Evaluations on an on-chip HSI automotive dataset reveal that MobileSACNet substantially surpasses state-of-the-art baselines, especially in complex road scenarios. Moreover, ablation studies illuminate the trade-off between computational efficiency and segmentation fidelity, offering critical insights for real-world deployment in intelligent transport and autonomous vehicle surveillance.
This paper proposes a binary classification framework for chronic endometritis diagnosis in hysteroscopy images. While hysteroscopy is less invasive than a histopathological examination, appearance feature-based diagnosis is not easy due to very similar appearance features of positive and negative samples observed in low-quality images. This work explores various components in our framework to explore the possibility of a less invasive diagnosis as a medical application of machine vision. Our experiments demonstrate that the classification performance of our framework is comparable to manual diagnosis using known symptoms in terms of several metrics.
Recognition of fine-grained actions involving similar tools and motions in egocentric videos is a challenging task in computer vision. In general, to distinguish such actions, humans often focus on the resultant state of the manipulated objects. From this perspective, we propose a novel fine-grained action recognition method that leverages object states in egocentric cooking videos through three key components: 1) precise extraction of the region containing only the food ingredient, reducing background noise; 2) selection of key frames ("End State frames") that clearly reflect the post-action state of the object; and 3) augmentation of training data using generated images. In this paper, we empirically demonstrate the effectiveness of the proposed method with our own dataset.
Laser Speckle Authentication (LSA) has been widely studied, yet the impact of the optical system size on its robustness against object misalignment remains underexplored. To clarify this, we evaluated the effects of perpendicular and parallel misalignments using six optical systems combining lens-based and lensless configurations. Our findings reveal that lens-based systems exhibit superior robustness against misalignment although they require a larger system size. This research provides insights for developing compact, robust authentication systems under practical size constraints.
