Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies

• URL: http://arxiv.org/abs/2308.01412v2
• Date: Sun, 12 Nov 2023 15:53:04 GMT
• Title: Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies
• Authors: Sergio Naval Marimont and Giacomo Tarroni
• Abstract summary: Unsupervised anomaly detection, or Out-of-Distribution detection, aims at identifying anomalous samples. Our method builds upon the self-supervised strategy consisting on training a segmentation network to identify local synthetic anomalies. Our contributions improve the synthetic anomaly generation process, making synthetic anomalies more heterogeneous.
• Score: 0.5677301320664404
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The detection and localization of anomalies is one important medical image analysis task. Most commonly, Computer Vision anomaly detection approaches rely on manual annotations that are both time consuming and expensive to obtain. Unsupervised anomaly detection, or Out-of-Distribution detection, aims at identifying anomalous samples relying only on unannotated samples considered normal. In this study we present a new unsupervised anomaly detection method. Our method builds upon the self-supervised strategy consisting on training a segmentation network to identify local synthetic anomalies. Our contributions improve the synthetic anomaly generation process, making synthetic anomalies more heterogeneous and challenging by 1) using complex random shapes and 2) smoothing the edges of synthetic anomalies so networks cannot rely on the high gradient between image and synthetic anomalies. In our implementation we adopted standard practices in 3D medical image segmentation, including 3D U-Net architecture, patch-wise training and model ensembling. Our method was evaluated using a validation set with different types of synthetic anomalies. Our experiments show that our method improved substantially the baseline method performance. Additionally, we evaluated our method by participating in the Medical Out-of-Distribution (MOOD) Challenge held at MICCAI in 2022 and achieved first position in both sample-wise and pixel-wise tasks. Our experiments and results in the latest MOOD challenge show that our simple yet effective approach can substantially improve the performance of Out-of-Distribution detection techniques which rely on synthetic anomalies.

Related papers

• Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection [88.34095233600719]
  FAPrompt is a novel framework designed to learn Fine-grained Abnormality Prompts for more accurate ZSAD. It substantially outperforms state-of-the-art methods by at least 3%-5% AUC/AP in both image- and pixel-level ZSAD tasks.
  arXiv  Detail & Related papers  (2024-10-14T08:41:31Z)
• DAS3D: Dual-modality Anomaly Synthesis for 3D Anomaly Detection [5.062312533373299]
  We propose a novel dual-modality augmentation method for 3D anomaly synthesis. We introduce a reconstruction-based discriminative anomaly detection network. Our method outperforms the state-of-the-art methods on detection precision and achieves competitive segmentation performance.
  arXiv  Detail & Related papers  (2024-10-13T12:38:16Z)
• Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly Detection [7.94529540044472]
  Unsupervised Anomaly Detection (UAD) methods aim to identify anomalies in test samples comparing them with a normative distribution learned from a dataset known to be anomaly-free. Approaches based on generative models offer interpretability by generating anomaly-free versions of test images, but are typically unable to identify subtle anomalies. We present a novel method that combines the strengths of both strategies: a generative cold-diffusion pipeline that is trained with the objective of turning synthetically-corrupted images back to their normal, original appearance.
  arXiv  Detail & Related papers  (2024-07-09T08:02:46Z)
• Spatial-aware Attention Generative Adversarial Network for Semi-supervised Anomaly Detection in Medical Image [63.59114880750643]
  We introduce a novel Spatial-aware Attention Generative Adrialversa Network (SAGAN) for one-class semi-supervised generation of health images. SAGAN generates high-quality health images corresponding to unlabeled data, guided by the reconstruction of normal images and restoration of pseudo-anomaly images. Extensive experiments on three medical datasets demonstrate that the proposed SAGAN outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-05-21T15:41:34Z)
• Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
  We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets. We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
  arXiv  Detail & Related papers  (2024-01-06T07:30:41Z)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• Prototypical Residual Networks for Anomaly Detection and Localization [80.5730594002466]
  We propose a framework called Prototypical Residual Network (PRN) PRN learns feature residuals of varying scales and sizes between anomalous and normal patterns to accurately reconstruct the segmentation maps of anomalous regions. We present a variety of anomaly generation strategies that consider both seen and unseen appearance variance to enlarge and diversify anomalies.
  arXiv  Detail & Related papers  (2022-12-05T05:03:46Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• Towards Fair Deep Anomaly Detection [24.237000220172906]
  We propose a new architecture for the fair anomaly detection approach (Deep Fair SVDD) We show that our proposed approach can remove the unfairness largely with minimal loss on the anomaly detection performance.
  arXiv  Detail & Related papers  (2020-12-29T22:34:45Z)
• OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning [23.48763375455514]
  We propose a One-for-all Image Anomaly Detection system based on disentangled learning using only clean samples. Our experiments with three datasets show that OIAD can detect over $90%$ of anomalies while maintaining a low false alarm rate.
  arXiv  Detail & Related papers  (2020-01-18T09:57:37Z)

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.