SALAD: Self-supervised Aggregation Learning for Anomaly Detection on X-Rays

Bozorgtabar, Behzad; Mahapatra, Dwarikanath; Vray, Guillaume; Thiran, Jean‐Philippe

doi:10.1007/978-3-030-59710-8_46

Cited by 35 publications

(23 citation statements)

References 13 publications

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“…a) Histopathology Image Analysis For Gleason Grading : Introduction of whole slide image (WSI) scanners has opened up the opportunity for computer-aided diagnosis (CAD) to aid pathologists and reduce inter-observer variability [45]- [56]. To overcome issue of high dimensional WSI commonly used methods [42], [57]- [67] apply patch-level image classification or use sliding windows.…”

Section: Related Workmentioning

confidence: 99%

Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation

Mahapatra¹

2021

Preprint

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Segmentation of Prostate Cancer (PCa) tissues from Gleason graded histopathology images is vital for accurate diagnosis. Although deep learning (DL) based segmentation methods achieve state-of-the-art accuracy, they rely on large datasets with manual annotations. We propose a method to synthesize for PCa histopathology images by learning the geometrical relationship between different disease labels using self-supervised learning. We use a weakly supervised segmentation approach that uses Gleason score to segment the diseased regions and the resulting segmentation map is used to train a Shape Restoration Network (ShaRe-Net) to predict missing mask segments in a selfsupervised manner. Using DenseUNet as the backbone generator architecture we incorporate latent variable sampling to inject diversity in the image generation process and thus improve robustness. Experiments on multiple histopathology datasets demonstrate the superiority of our method over competing image synthesis methods for segmentation tasks. Ablation studies show the benefits of integrating geometry and diversity in generating high-quality images, and our self-supervised approach with limited class-labeled data achieves similar performance as fully supervised learning.

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Section: Related Workmentioning

confidence: 99%

Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation

Mahapatra¹

2021

Preprint

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“…The gap between abnormal image and abnormal SI is larger than that between normal ones. retinal OCT [4], [35], [36], [37], chest X-Ray [38], [39], [40], [41] and brain CT [42]. In this paper, we focus on the reconstruction-based anomaly detection in medical images.…”

Section: Related Workmentioning

confidence: 99%

Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images

Zhang¹,

Li²,

Luo³

et al. 2021

Preprint

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Anomaly detection in medical images refers to the identification of abnormal images with only normal images in the training set. Most existing methods solve this problem with a self-reconstruction framework, which tends to learn an identity mapping and reduces the sensitivity to anomalies. To mitigate this problem, in this paper, we propose a novel Proxy-bridged Image Reconstruction Network (ProxyAno) for anomaly detection in medical images. Specifically, we use an intermediate proxy to bridge the input image and the reconstructed image. We study different proxy types, and we find that the superpixel-image (SI) is the best one. We set all pixels' intensities within each superpixel as their average intensity, and denote this image as SI. The proposed ProxyAno consists of two modules, a Proxy Extraction Module and an Image Reconstruction Module. In the Proxy Extraction Module, a memory is introduced to memorize the feature correspondence for normal image to its corresponding SI, while the memorized correspondence does not apply to the abnormal images, which leads to the information loss for abnormal image and facilitates the anomaly detection. In the Image Reconstruction Module, we map an SI to its reconstructed image. Further, we crop a patch from the image and paste it on the normal SI to mimic the anomalies, and enforce the network to reconstruct the normal image even with the pseudo abnormal SI. In this way, our network enlarges the reconstruction error for anomalies. Extensive experiments on brain MR images, retinal OCT images and retinal fundus images verify the effectiveness of our method for both image-level and pixel-level anomaly detection.

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“…β-VAE have also been successfully used to model the inter-individual variability in the mouse brain [14]. More recently, very promising self-supervised methods have been applied to anomaly detection problems in medical images [6], but these methods lack the generative aspect provided by GAN and VAE, which is crucial in terms of explainability. All these works assessed images containing known lesions.…”

Section: Introductionmentioning

confidence: 99%

Detection of Abnormal Folding Patterns with Unsupervised Deep Generative Models

Guillon

Cagna

Dufumier

et al. 2021

Lecture Notes in Computer Science

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Although the main structures of cortical folding are present in each human brain, the folding pattern is unique to each individual. Because of this large normal variability, the identification of abnormal patterns associated to developmental disorders is a complex open challenge. In this paper, we tackle this problem as an anomaly detection task and explore the potential of deep generative models using benchmarks made up of synthetic anomalies. To focus learning on the folding geometry, brain MRI are preprocessed first to deal only with a skeleton-based negative cast of the cortex. A variational auto-encoder is trained to get a representation of the regional variability of the folding pattern of the general population. Then several synthetic benchmark datasets of abnormalities are designed. The latent space expressivity is assessed through classification experiments between control's and abnormal's latent codes. Finally, the properties encoded in the latent space are analyzed through perturbation of specific latent dimensions and observation of the resulting modification of the reconstructed images. The results have shown that the latent representation is rich enough to distinguish subtle differences like asymmetries between the right and left hemispheres.

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SALAD: Self-supervised Aggregation Learning for Anomaly Detection on X-Rays

Cited by 35 publications

References 13 publications

Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation

Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation

Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images

Detection of Abnormal Folding Patterns with Unsupervised Deep Generative Models

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