AREPAS: Anomaly Detection in Fine-Grained Anatomy with Reconstruction-Based Semantic Patch-Scoring

• URL: http://arxiv.org/abs/2509.12905v1
• Date: Tue, 16 Sep 2025 09:59:47 GMT
• Title: AREPAS: Anomaly Detection in Fine-Grained Anatomy with Reconstruction-Based Semantic Patch-Scoring
• Authors: Branko Mitic, Philipp Seeböck, Helmut Prosch, Georg Langs,
• Abstract summary: Normal fine-grained tissue variability is a major challenge for existing generative anomaly detection methods.<n>We propose a novel generative AD approach addressing this issue.<n>It consists of an image-to-image translation for anomaly-free reconstruction and a subsequent patch similarity scoring between observed and generated image-pairs for precise anomaly localization.<n>Results show improved pixel-level anomaly segmentation in both chest CTs and brain MRIs, with relative DICE score improvements of +1.9% and +4.4%, respectively, compared to other state-of-the-art reconstruction-based methods.
• Score: 0.6001999957263661
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Early detection of newly emerging diseases, lesion severity assessment, differentiation of medical conditions and automated screening are examples for the wide applicability and importance of anomaly detection (AD) and unsupervised segmentation in medicine. Normal fine-grained tissue variability such as present in pulmonary anatomy is a major challenge for existing generative AD methods. Here, we propose a novel generative AD approach addressing this issue. It consists of an image-to-image translation for anomaly-free reconstruction and a subsequent patch similarity scoring between observed and generated image-pairs for precise anomaly localization. We validate the new method on chest computed tomography (CT) scans for the detection and segmentation of infectious disease lesions. To assess generalizability, we evaluate the method on an ischemic stroke lesion segmentation task in T1-weighted brain MRI. Results show improved pixel-level anomaly segmentation in both chest CTs and brain MRIs, with relative DICE score improvements of +1.9% and +4.4%, respectively, compared to other state-of-the-art reconstruction-based methods.

Related papers

• Unsupervised Anomaly Detection in Brain MRI via Disentangled Anatomy Learning [13.28586148664722]
  Two novel modules are proposed, forming a new PHI reconstruction framework.<n>The disentangled representation module is proposed to improve generalizability by decoupling brain MRI into imaging information.<n>The edge-to-image restoration module is designed to reconstruct high-quality PHIs by restoring the anatomical representation from the high-frequency edge information.
  arXiv  Detail & Related papers  (2025-12-26T08:39:09Z)
• PDSE: A Multiple Lesion Detector for CT Images using PANet and Deformable Squeeze-and-Excitation Block [10.563907026873443]
  We introduce a one-stage lesion detection framework, PDSE, by redesigning Retinanet.<n>We enhance the path aggregation flow by incorporating a low-level feature map.<n>Our algorithm achieved an mAP of over 0.20 on the public DeepLesion benchmark.
  arXiv  Detail & Related papers  (2025-06-04T06:38:31Z)
• Rethinking Medical Anomaly Detection in Brain MRI: An Image Quality Assessment Perspective [14.39502951611029]
  We propose a fusion quality loss function that combines Structural Similarity Index Measure loss with l1 loss, offering a more comprehensive evaluation of reconstruction quality. We also introduce a data pre-processing strategy that enhances the average intensity ratio (AIR) between normal and abnormal regions, further improving the distinction of anomalies. The proposed IQA approach achieves significant improvements (>10%) in terms of Dice coefficient (DICE) and Area Under the Precision-Recall Curve (AUPRC) on the BraTS21 (T2, FLAIR) and MSULB datasets.
  arXiv  Detail & Related papers  (2024-08-15T15:55:07Z)
• Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images [39.94162291765236]
  We present a weakly supervised method to generate a healthy version of a diseased image and then use it to obtain a pixel-wise anomaly map. We employ a diffusion model trained on healthy samples and combine Denoising Diffusion Probabilistic Model (DDPM) and Denoising Implicit Model (DDIM) at each step of the sampling process.
  arXiv  Detail & Related papers  (2023-08-03T21:56:50Z)
• AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection [34.007468043336274]
  We propose a robust inpainting model to learn the details of healthy anatomies and reconstruct high-resolution images. We also propose an autoinpainting pipeline to automatically detect tumors, replace their appearance with the learned healthy anatomies, and based on that segment the tumoral volumes.
  arXiv  Detail & Related papers  (2023-05-21T05:45:38Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Employing similarity to highlight differences: On the impact of anatomical assumptions in chest X-ray registration methods [2.080328156648695]
  We develop an anatomically penalized convolutional multi-stage solution on the National Institutes of Health (NIH) data set. Our method proves to be a natural way to limit the folding percentage of the warp field to 1/6 of the state of the art. We statistically evaluate the benefits of our method and highlight the limits of currently used metrics for registration of chest X-rays.
  arXiv  Detail & Related papers  (2023-01-23T09:42:49Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Lesion detection in contrast enhanced spectral mammography [0.0]
  The recent emergence of neural networks models for the analysis of breast images has been a breakthrough in computer aided diagnostic. This work proposes a deep-learning-based Computer Aided Diagnostic development for CESM recombined images able to detect lesions and classify cases.
  arXiv  Detail & Related papers  (2022-07-20T06:49:02Z)
• Context-Aware Transformers For Spinal Cancer Detection and Radiological Grading [70.04389979779195]
  This paper proposes a novel transformer-based model architecture for medical imaging problems involving analysis of vertebrae. It considers two applications of such models in MR images: (a) detection of spinal metastases and the related conditions of vertebral fractures and metastatic cord compression. We show that by considering the context of vertebral bodies in the image, SCT improves the accuracy for several gradings compared to previously published model.
  arXiv  Detail & Related papers  (2022-06-27T10:31:03Z)
• SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection [76.01333073259677]
  We propose the use of Space-aware Memory Queues for In-painting and Detecting anomalies from radiography images (abbreviated as SQUID) We show that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns; and in the inference, it can identify anomalies (unseen/modified patterns) in the image.
  arXiv  Detail & Related papers  (2021-11-26T13:47:34Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images [61.862364277007934]
  We propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images. A multi-task deep network (called M$2$UNet) is then developed to assess the severity of COVID-19 patients. Our M$2$UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment.
  arXiv  Detail & Related papers  (2020-05-08T03:16:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.