Unsupervised anomaly detection methods primarily focus on modeling the distribution of normal samples at image/feature level. Significant deviation from the modeled distribution is then considered as anomaly. Yet, each normal sample may have its own unique characteristic drifting from the idea distribution, making it difficult to distinguish between anomaly and characteristic. In this paper, we propose a memory-based Coarse-to-Fine Feature Fusion (C3F) module to tackle this challenge. Specifically, we construct a group of memory banks that model the feature distribution of normal samples at various levels of granularity. The memory-based C3F is applied to each skip-connection between the encoder and the decoder, and progressively removes anomaly while maintaining characteristic. This helps to reconstruct defect-free image with characteristic preserved, encouraging large (respsmall) deviation from the modeled distribution for anomaly (respcharacteristic). Besides, we also introduce a novel rough anomaly score map Guided Segmentation (GS) module to achieve precise anomaly localization. Extensive experiments on widely used VisA and MVTec-AD benchmarks demonstrate the wide-ranging applicability of the proposed method termed C3FGS on industrial components of various forms. The implementation code is publicly available athttps://github.com/LZL501/c3f_industrial_anomaly_detection.

Many keypoint detection and description methods have been proposed for image matching or registration. While these methods demonstrate promising performance for single-modality image matching, they often struggle with multimodal data because the descriptors trained on single-modality data tend to lack robustness against the non-linear variations present in multimodal data. Extending such methods to multimodal image matching often requires well-aligned multimodal data to learn modality-invariant descriptors. However, acquiring such data is often costly and impractical in many real-world scenarios. To address this challenge, we propose a modality-invariant feature learning network (MIFNet) to compute modality-invariant features for keypoint descriptions in multimodal image matching using only single-modality training data. Specifically, we propose a novel latent feature aggregation module and a cumulative hybrid aggregation module to enhance the base keypoint descriptors trained on single-modality data by leveraging pre-trained features from Stable Diffusion models. We validate our method with recent keypoint detection and description methods in three multimodal retinal image datasets (CF-FA, CF-OCT, EMA-OCTA) and two remote sensing datasets (Optical-SAR and Optical-NIR). Extensive experiments demonstrate that the proposed MIFNet is able to learn modality-invariant feature for multimodal image matching without accessing the targeted modality and has good zero-shot generalization ability. The code will be released at https://github.com/lyp-deeplearning/MIFNet.

Annotating 3D medical images demands expert knowledge and is time-consuming. As a result, semi-supervised learning (SSL) approaches have gained significant interest in 3D medical image segmentation. The significant size differences among various organs in the human body lead to imbalanced class distribution, which is a major challenge in the real-world application of these SSL approaches. To address this issue, we develop a novel Shape Transformation driven by Active Contour (STAC), that enlarges smaller organs to alleviate imbalanced class distribution across different organs. Inspired by curve evolution theory in active contour methods, STAC employs a signed distance function (SDF) as the level set function, to implicitly represent the shape of organs, and deforms voxels in the direction of the steepest descent of SDF (i.e., the normal vector). To ensure that the voxels far from expansion organs remain unchanged, we design an SDF-based weight function to control the degree of deformation for each voxel. We then use STAC as a data-augmentation process during the training stage. Experimental results on two benchmark datasets demonstrate that the proposed method significantly outperforms some state-of-the-art methods. Source code is publicly available at https://github.com/GuGuLL123/STAC.