Traditional image classification methods struggle with the skewed distribution of medical images, where some diseases are overrepresented while others are underrepresented. In light of the diverse learning challenges presented by different medical image samples, we introduce a novel approach to long-tailed medical image classification through a re-weighting strategy based on sample hardness. Our method differs from existing strategies by determining a sample’s weight in the loss function based solely on its hardness - a measure of the sample’s learning difficulty. This approach simplifies the training process, avoids heavy reliance on hyper-parameters, and enhances the model’s generalizability and robustness, especially in the context of long-tailed distributions common in medical image datasets. We validate the effectiveness of our method with comprehensive experiments on the long-tailed medical datasets, demonstrating significant improvements in classification performance compared to existing methods.

Recent research has explored using diffusion models for out-of-distribution (OOD) detection, leveraging their ability to distinguish OOD samples by measuring reconstruction errors. Existing methods use visual feature metrics to measure reconstruction errors. However, the inherent stochasticity in diffusion models introduces variability, distorting reconstruction error measurements with visual feature metrics. To address this issue, we propose Classifier Probability Measurement via Diffusion Models (CPMDiff), a novel OOD detection method that measures the classifier probabilities’ discrepancy between input and reconstructed data. By leveraging classifier space measurement, CPMDiff alleviates the distortion in reconstruction error metrics caused by the stochasticity of diffusion models. Experiments on benchmark datasets demonstrate that CPMDiff significantly improves the performance of diffusion model-based methods in OOD detection, especially on challenging datasets.

Abstract Aiming at the problem of low detection accuracy caused by overlapping occlusion and noise disturbance, a contraband detection algorithm for X‐ray security inspection images based on global semantic enhancement is proposed to achieve accurate contraband target detection by enhancing global semantic information. First, the disturbance suppression module is used to weaken the noise disturbance at different positions by local suppression and aggregate finer detail information. Then, the parallel cascade search module is used to capture long‐range dependencies and strengthen the representation of global semantic information, which helps the model identify contraband under overlapping occlusion. Finally, the contribution of different features is adaptively adjusted through the feature‐weighted fusion module, which promotes the effective fusion of multi‐scale features and improves the accuracy of model detection. The method in this article has been extensively evaluated and experimented on three mainstream benchmark datasets: SIXray, OPIXray, and PIDray. The mAPs of three datasets reach 93.5%, 91.9%, and 85.9%, respectively. The experimental results fully demonstrate that the method in this article has better performance compared with the latest method, which can meet the practical application requirements of real‐time target detection.

Few-shot learning with$N$-way$K$-shot scheme is an open challenge in machine learning. Many metric-based approaches have been proposed to tackle this problem, e.g., the Matching Networks and CLIP-Adapter. Despite that these approaches have shown significant progress, the mechanism of why these methods succeed has not been well explored. In this paper, we try to interpret these metric-based few-shot learning methods via causal mechanism. We show that the existing approaches can be viewed as specific forms of front-door adjustment, which can alleviate the effect of spurious correlations and thus learn the causality. This causal interpretation could provide us a new perspective to better understand these existing metric-based methods. Further, based on this causal interpretation, we simply introduce two causal methods for metric-based few-shot learning, which considers not only the relationship between examples but also the diversity of representations. Experimental results demonstrate the superiority of our proposed methods in few-shot classification on various benchmark datasets. Code is available inhttps://github.com/lingl1024/causalFewShot.