ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising

• URL: http://arxiv.org/abs/2307.12225v1
• Date: Sun, 23 Jul 2023 04:36:05 GMT
• Title: ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising
• Abstract summary: We propose a novel Anatomy-aware Supervised CONtrastive learning framework, termed ASCON, to explore the anatomical semantics for low-dose CT denoising. Our ASCON provides anatomical interpretability for low-dose CT denoising for the first time.
• Score: 23.274928463320986
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: While various deep learning methods have been proposed for low-dose computed tomography (CT) denoising, most of them leverage the normal-dose CT images as the ground-truth to supervise the denoising process. These methods typically ignore the inherent correlation within a single CT image, especially the anatomical semantics of human tissues, and lack the interpretability on the denoising process. In this paper, we propose a novel Anatomy-aware Supervised CONtrastive learning framework, termed ASCON, which can explore the anatomical semantics for low-dose CT denoising while providing anatomical interpretability. The proposed ASCON consists of two novel designs: an efficient self-attention-based U-Net (ESAU-Net) and a multi-scale anatomical contrastive network (MAC-Net). First, to better capture global-local interactions and adapt to the high-resolution input, an efficient ESAU-Net is introduced by using a channel-wise self-attention mechanism. Second, MAC-Net incorporates a patch-wise non-contrastive module to capture inherent anatomical information and a pixel-wise contrastive module to maintain intrinsic anatomical consistency. Extensive experimental results on two public low-dose CT denoising datasets demonstrate superior performance of ASCON over state-of-the-art models. Remarkably, our ASCON provides anatomical interpretability for low-dose CT denoising for the first time. Source code is available at https://github.com/hao1635/ASCON.

Related papers

• A deep dictionary network-based foundation model for ultra-low-dose CT denoising [49.70293041079032]
  Ultra-low-dose computed tomography (ULDCT) reduces radiation exposure but suffers from severe noise that degrades diagnostic image quality.<n>Existing deep learning-based denoising methods are typically trained in an organ-specific fashion, resulting in limited generalization across heterogeneous multi?organ imaging scenarios.<n>We propose an architecture?able foundation model based on the deep dictionary network (DDN) for unified multi-organ ULDCT denoising.
  arXiv  Detail & Related papers  (2026-09-11T05:14:08Z)
• DeVAR: Low-Dose CT Denoising via Visual Autoregressive Modeling [14.938419722549575]
  Low-dose CT (LDCT) protocols reduce radiation risks but inevitably suffer from severe noise and artifacts that compromise diagnostic accuracy.<n>We propose a novel generative framework that applies visual autore modeling to LDCT denoising for the first time.<n>Our hybrid NDCT decoder seamlessly integrates continuous and discrete latents to reconstruct high-fidelity, detail-preserved images.
  arXiv  Detail & Related papers  (2026-06-26T09:55:37Z)
• PHASOR: Anatomy- and Phase-Consistent Volumetric Diffusion for CT Virtual Contrast Enhancement [60.81732730684265]
  This paper introduces PHASOR, a volumetric diffusion framework for high-fidelity virtual contrast enhancement (VCE)<n>By treating CT volumes as coherent sequences, we leverage a video diffusion model to enhance structural coherence and volumetric accuracy.<n>To ensure anatomy-phase consistent synthesis, we introduce two complementary modules. First, anatomy-routed mixture-of-experts (AR-MoE) anchors distinct enhancement patterns to anatomical semantics, with organ-specific memory to capture salient details.<n>Second, intensity-phase aware representation alignment (IP-REPA) highlights intricate contrast signals while mitigating the impact of imperfect spatial alignment.
  arXiv  Detail & Related papers  (2026-04-01T15:57:18Z)
• Structure-constrained Language-informed Diffusion Model for Unpaired Low-dose Computed Tomography Angiography Reconstruction [72.80209358480424]
  overdose of iodinated contrast media (ICM) can cause kidney damage and life-threatening allergic reactions.<n>Deep learning methods can generate CT images of normal-dose ICM from low-dose ICM, reducing the required dose.<n>We propose a Structure-constrained Language-informed Diffusion Model (SLDM) that integrates structural synergy and spatial intelligence.
  arXiv  Detail & Related papers  (2026-01-28T06:54:06Z)
• A Denoising Framework for Real-World Ultra-Low Dose Lung CT Images Based on an Image Purification Strategy [1.6574413179773761]
  Ultra-low dose CT (uLDCT) significantly reduces radiation exposure but introduces severe noise and artifacts.<n>It also leads to substantial spatial misalignment between uLDCT and normal dose CT (NDCT) image pairs.<n>This paper proposes an innovative denoising framework based on an Image Purification (IP) strategy.
  arXiv  Detail & Related papers  (2025-10-08T19:40:38Z)
• FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising [55.04342933312839]
  We propose FoundDiff, a foundational diffusion model for unified and generalizable low-dose computed tomography (CT) denoising.<n>FoundDiff employs a two-stage strategy: (i) dose-anatomy perception and (ii) adaptive denoising.<n>First, we develop a dose- and anatomy-aware contrastive language image pre-training model (DA-CLIP) to achieve robust dose and anatomy perception.<n>Second, we design a dose- and anatomy-aware diffusion model (DA-Diff) to perform adaptive and generalizable denoising.
  arXiv  Detail & Related papers  (2025-08-24T11:03:56Z)
• Anatomy-Aware Low-Dose CT Denoising via Pretrained Vision Models and Semantic-Guided Contrastive Learning [12.975922919920393]
  We propose ALDEN, an anatomy-aware LDCT denoising method that integrates semantic features of pretrained vision models with adversarial and contrastive learning.<n>Specifically, we introduce an anatomy-aware discriminator that dynamically fuses hierarchical semantic features from reference normal-dose CT (NDCT) via cross-attention mechanisms.<n>In addition, we propose a semantic-guided contrastive learning module that enforces anatomical consistency by contrasting PVM-derived features from LDCT, denoised CT and NDCT, preserving tissue-specific patterns through positive pairs and suppressing artifacts via dual negative pairs.
  arXiv  Detail & Related papers  (2025-08-11T09:17:12Z)
• WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising [74.14134385961775]
  We introduce a novel self-supervised CT image denoising method called WIA-LD2ND, only using NDCT data. WIA-LD2ND comprises two modules: Wavelet-based Image Alignment (WIA) and Frequency-Aware Multi-scale Loss (FAM)
  arXiv  Detail & Related papers  (2024-03-18T11:20:11Z)
• Low-dose CT Denoising with Language-engaged Dual-space Alignment [21.172319554618497]
  We propose a plug-and-play Language-Engaged Dual-space Alignment loss (LEDA) to optimize low-dose CT denoising models. Our idea is to leverage large language models (LLMs) to align denoised CT and normal dose CT images in both the continuous perceptual space and discrete semantic space. LEDA involves two steps: the first is to pretrain an LLM-guided CT autoencoder, which can encode a CT image into continuous high-level features and quantize them into a token space to produce semantic tokens.
  arXiv  Detail & Related papers  (2024-03-10T08:21:50Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoising [11.67382017798666]
  Low-dose computed tomography (LDCT) denoising is an important problem in CT research. vision transformers have shown superior feature representation ability over convolutional neural networks (CNNs) We propose a Convolution-free Token2Token Dilated Vision Transformer for low-dose CT denoising.
  arXiv  Detail & Related papers  (2022-02-28T02:58:16Z)
• InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images [53.4351366246531]
  We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
  arXiv  Detail & Related papers  (2021-12-23T15:52:37Z)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism [4.998352078907441]
  Low radiation dose may result in increased noise and artifacts, which greatly affected the clinical diagnosis. To obtain high-quality Total-body Low-dose CT (LDCT) images, previous deep-learning-based research work has introduced various network architectures. In this paper, we propose a novel intra-task knowledge transfer method that leverages the distilled knowledge from NDCT images.
  arXiv  Detail & Related papers  (2021-12-01T06:46:38Z)
• Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis [65.47507533905188]
  Conditional generative adversarial networks have been applied to generate synthetic histopathology images. We propose a sharpness loss regularized generative adversarial network to synthesize realistic histopathology images.
  arXiv  Detail & Related papers  (2021-10-27T18:54:25Z)
• Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images [50.55978219682419]
  We propose a symmetry enhanced attention network (SEAN) for acute ischemic infarct segmentation. Our proposed network automatically transforms an input CT image into the standard space where the brain tissue is bilaterally symmetric. The proposed SEAN outperforms some symmetry-based state-of-the-art methods in terms of both dice coefficient and infarct localization.
  arXiv  Detail & Related papers  (2021-10-11T07:13:26Z)
• DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising [22.351540738281265]
  Deep learning techniques have been introduced to improve the image quality of LDCT images through denoising. This paper proposes a novel method, termed DU-GAN, which leverages U-Net based discriminators in the GANs framework to learn both global and local difference between the denoised and normal-dose images.
  arXiv  Detail & Related papers  (2021-08-24T14:37:46Z)

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.