Diffusion Models for Medical Anomaly Detection

Wolleb, Julia; Bieder, Florentin; Sandkühler, Robin; Cattin, Philippe C.

doi:10.1007/978-3-031-16452-1_4

Cited by 136 publications

(89 citation statements)

References 19 publications

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“…Recently, a wide range of diffusion-based perception applications has emerged, such as image generation [15,21,35,47], image segmentation [4,7,18,23], object detection [8], etc. A few previous works have tentatively explored the application of diffusion in medical anomaly detection [32,53], such as AnoDDPM [21], but its inference speed is slow and the anomalous false positive rate is high, which is nowhere near the requirements for practical deployment. To the best of our knowledge, we are the first to propose a diffusion-based pipeline with gratifying anomaly detection and localization performance as well as fast inference speed.…”

Section: Related Workmentioning

confidence: 99%

DiffusionAD: Denoising Diffusion for Anomaly Detection

Zhang¹,

Wang²,

Wu³

et al. 2023

Preprint

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Anomaly detection is widely applied due to its remarkable effectiveness and efficiency in meeting the needs of realworld industrial manufacturing. We introduce a new pipeline, DiffusionAD, to anomaly detection. We frame anomaly detection as a "noise-to-norm" paradigm, in which anomalies are identified as inconsistencies between a query image and its flawless approximation. Our pipeline achieves this by restoring the anomalous regions from the noisy corrupted query image while keeping the normal regions unchanged. DiffusionAD includes a denoising sub-network and a segmentation sub-network, which work together to provide intuitive anomaly detection and localization in an end-to-end manner, without the need for complicated post-processing steps. Remarkably, during inference, this framework delivers satisfactory performance with just one diffusion reverse process step, which is tens to hundreds of times faster than general diffusion methods. Extensive evaluations on standard and challenging benchmarks including VisA and DAGM show that DiffusionAD outperforms current state-of-the-art paradigms, demonstrating the effectiveness and generalizability of the proposed pipeline.

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Section: Related Workmentioning

confidence: 99%

DiffusionAD: Denoising Diffusion for Anomaly Detection

Zhang¹,

Wang²,

Wu³

et al. 2023

Preprint

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“…For instance, Med-SegDiff [26] achieves 2D medical image segmentation by segmenting Denoising-UNet, and interacting with inter-structural information through Fourier transform. Wolleb et al [25] employ the diffusion model to solve the 2D medical image segmentation problem and improve the robustness of the segmentation results by fusing the output results of each diffusion step using a summation manner during testing. However, these methods are limited to 2D segmentation, and the diffusion model cannot generate multi-label segmentation directly.…”

Section: Introductionmentioning

confidence: 99%

Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation

Xing¹,

Liang²,

Fu³

et al. 2023

Preprint

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In recent years, Denoising Diffusion Models have demonstrated remarkable success in generating semantically valuable pixel-wise representations for image generative modeling. In this study, we propose a novel end-to-end framework, called Diff-UNet, for medical volumetric segmentation. Our approach integrates the diffusion model into a standard U-shaped architecture to extract semantic information from the input volume effectively, resulting in excellent pixel-level representations for medical volumetric segmentation. To enhance the robustness of the diffusion model's prediction results, we also introduce a Step-Uncertainty based Fusion (SUF) module during inference to combine the outputs of the diffusion models at each step. We evaluate our method on three datasets, including multimodal brain tumors in MRI, liver tumors, and multi-organ CT volumes, and demonstrate that Diff-UNet outperforms other state-of-the-art methods significantly. Our experimental results also indicate the universality and effectiveness of the proposed model. The proposed framework has the potential to facilitate the accurate diagnosis and treatment of medical conditions by enabling more precise segmentation of anatomical structures. The codes of Diff-UNet are available at https://github.com/ge-xing/Diff-UNet.

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“…Specifically, DDPM is a generative model based on a Markov chain, which models the data distribution by simulating a diffusion process that evolves the input data towards a target distribution. Although a few pioneer works tried to adopt the diffusion model for image segmentation and object detection tasks [1,29,4,12], their potential for high-level vision has yet to be fully explored.…”

Section: Introductionmentioning

confidence: 99%

DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification

Yao¹,

Fu²,

Avilés-Rivero³

et al. 2023

Preprint

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Diffusion Probabilistic Models have recently shown remarkable performance in generative image modeling, attracting significant attention in the computer vision community. However, while a substantial amount of diffusion-based research has focused on generative tasks, few studies have applied diffusion models to general medical image classification. In this paper, we propose the first diffusion-based model (named DiffMIC) to address general medical image classification by eliminating unexpected noise and perturbations in medical images and robustly capturing semantic representation. To achieve this goal, we devise a dual conditional guidance strategy that conditions each diffusion step with multiple granularities to improve step-wise regional attention. Furthermore, we propose learning the mutual information in each granularity by enforcing Maximum-Mean Discrepancy regularization during the diffusion forward process. We evaluate the effectiveness of our DiffMIC on three medical classification tasks with different image modalities, including placental maturity grading on ultrasound images, skin lesion classification using dermatoscopic images, and diabetic retinopathy grading using fundus images. Our experimental results demonstrate that DiffMIC outperforms state-of-the-art methods by a significant margin, indicating the universality and effectiveness of the proposed model. Our code will be publicly available at https://github.com/scott-yjyang/DiffMIC.

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Diffusion Models for Medical Anomaly Detection

Cited by 136 publications

References 19 publications

DiffusionAD: Denoising Diffusion for Anomaly Detection

DiffusionAD: Denoising Diffusion for Anomaly Detection

Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation

DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification

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