Conventional imaging diagnostics frequently encounter bottlenecks due to
manual inspection, which can lead to delays and inconsistencies. Although deep
learning offers a pathway to automation and enhanced accuracy, foundational
models in computer vision often emphasize global context at the expense of
local details, which are vital for medical imaging diagnostics. To address
this, we harness the Swin Transformer's capacity to discern extended spatial
dependencies within images through the hierarchical framework. Our novel
contribution lies in refining local feature representations, orienting them
specifically toward the final distribution of the classifier. This method
ensures that local features are not only preserved but are also enriched with
task-specific information, enhancing their relevance and detail at every
hierarchical level. By implementing this strategy, our model demonstrates
significant robustness and precision, as evidenced by extensive validation of
two established benchmarks for Knee OsteoArthritis (KOA) grade classification.
These results highlight our approach's effectiveness and its promising
implications for the future of medical imaging diagnostics. Our implementation
is available on this https URL

通过使用 Swin Transformer 以及局部特征表示的改进方法，本研究在骨关节炎等医学影像诊断领域实现了显著的鲁棒性和精度提升。

从全局语义到局部显著特征的转变：用于膝骨关节炎严重度评估的 Swin-Transformer

Shifting Focus: From Global Semantics to Local Prominent Features in  Swin-Transformer for Knee Osteoarthritis Severity Assessment

Cross-modality person re-identification is a challenging problem which
retrieves a given pedestrian image in RGB modality among all the gallery images
in infrared modality. The task can address the limitation of RGB-based person
Re-ID in dark environments. Existing researches mainly focus on enlarging
inter-class differences of feature to solve the problem. However, few studies
investigate improving intra-class cross-modality similarity, which is important
for this issue. In this paper, we propose a novel loss function, called
Hetero-Center loss (HC loss) to reduce the intra-class cross-modality
variations. Specifically, HC loss can supervise the network learning the
cross-modality invariant information by constraining the intra-class center
distance between two heterogenous modalities. With the joint supervision of
Cross-Entropy (CE) loss and HC loss, the network is trained to achieve two
vital objectives, inter-class discrepancy and intra-class cross-modality
similarity as much as possible. Besides, we propose a simple and
high-performance network architecture to learn local feature representations
for cross-modality person re-identification, which can be a baseline for future
research. Extensive experiments indicate the effectiveness of the proposed
methods, which outperform state-of-the-art methods by a wide margin.

本文提出了一种新的损失函数，叫做 Hetero-Center loss（HC loss），用于减少跨模态内类别的差异，并提出了一种简单高效的网络架构，用于学习跨模态人员重新识别的本地特征表示，得到了比现有方法更好的结果。