A-IDE : Agent-Integrated Denoising Experts

• URL: http://arxiv.org/abs/2503.16780v1
• Date: Fri, 21 Mar 2025 01:26:54 GMT
• Title: A-IDE : Agent-Integrated Denoising Experts
• Authors: Uihyun Cho, Namhun Kim,
• Abstract summary: We introduce textbfAgent-Integrated Denoising Experts (A-IDE) framework, which integrates three anatomical region-specialized RED-CNN models.<n>A-IDE achieves superior performance in RMSE, PSNR, and SSIM compared to a single unified denoiser.
• Score: 0.46040036610482665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in deep-learning based denoising methods have improved Low-Dose CT image quality. However, due to distinct HU distributions and diverse anatomical characteristics, a single model often struggles to generalize across multiple anatomies. To address this limitation, we introduce \textbf{Agent-Integrated Denoising Experts (A-IDE)} framework, which integrates three anatomical region-specialized RED-CNN models under the management of decision-making LLM agent. The agent analyzes semantic cues from BiomedCLIP to dynamically route incoming LDCT scans to the most appropriate expert model. We highlight three major advantages of our approach. A-IDE excels in heterogeneous, data-scarce environments. The framework automatically prevents overfitting by distributing tasks among multiple experts. Finally, our LLM-driven agentic pipeline eliminates the need for manual interventions. Experimental evaluations on the Mayo-2016 dataset confirm that A-IDE achieves superior performance in RMSE, PSNR, and SSIM compared to a single unified denoiser.

Related papers

• RAD: Towards Trustworthy Retrieval-Augmented Multi-modal Clinical Diagnosis [56.373297358647655]
  Retrieval-Augmented Diagnosis (RAD) is a novel framework that injects external knowledge into multimodal models directly on downstream tasks.<n>RAD operates through three key mechanisms: retrieval and refinement of disease-centered knowledge from multiple medical sources, a guideline-enhanced contrastive loss transformer, and a dual decoder.
  arXiv  Detail & Related papers  (2025-09-24T10:36:14Z)
• Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation [13.530424405137417]
  Semi-supervised learning has been employed to alleviate the need for extensive labeled data for histopathology image segmentation.<n>Existing methods struggle with noisy pseudo-labels due to ambiguous gland boundaries and morphological misclassification.<n>This paper introduces Semi-MOE, to the best of our knowledge, the first multi-task Mixture-of-Experts framework for semi-supervised histopathology image segmentation.
  arXiv  Detail & Related papers  (2025-09-17T09:03:04Z)
• Unified Supervision For Vision-Language Modeling in 3D Computed Tomography [1.4193731654133002]
  General-purpose vision-language models (VLMs) have emerged as promising tools in radiology, offering zero-shot capabilities.<n>In high-stakes domains like diagnostic radiology, these models often lack the discriminative precision required for reliable clinical use.<n>We introduce Uniferum, a volumetric VLM that unifies diverse supervision signals, encoded in classification labels and segmentation masks, into a single training framework.
  arXiv  Detail & Related papers  (2025-09-01T15:30:17Z)
• 2D Ultrasound Elasticity Imaging of Abdominal Aortic Aneurysms Using Deep Neural Networks [0.5249805590164902]
  Abdominal aortic aneurysms (AAA) pose a significant clinical risk due to their potential for rupture.<n>We propose a deep learning-based framework for elasticity imaging of AAAs with 2D ultrasound.
  arXiv  Detail & Related papers  (2025-08-25T21:42:54Z)
• 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)
• ForenX: Towards Explainable AI-Generated Image Detection with Multimodal Large Language Models [82.04858317800097]
  We present ForenX, a novel method that not only identifies the authenticity of images but also provides explanations that resonate with human thoughts.<n>ForenX employs the powerful multimodal large language models (MLLMs) to analyze and interpret forensic cues.<n>We introduce ForgReason, a dataset dedicated to descriptions of forgery evidences in AI-generated images.
  arXiv  Detail & Related papers  (2025-08-02T15:21:26Z)
• CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesis [2.333160549379721]
  High dimensionality and structural complexity of 3D neuroimaging data pose challenges for MRI-to-PET translation.<n>We present CoCoLIT, a diffusion-based latent generative framework that incorporates three main innovations.<n>We evaluate CoCoLIT's performance on publicly available datasets and find that our model significantly outperforms state-of-the-art methods on both image-based and amyloid-related metrics.
  arXiv  Detail & Related papers  (2025-08-02T09:58:30Z)
• C3S3: Complementary Competition and Contrastive Selection for Semi-Supervised Medical Image Segmentation [2.3441453628908238]
  We introduce C3S3, a novel semi-supervised segmentation model that integrates complementary competition and contrastive selection.<n>This design significantly sharpens boundary delineation and enhances overall precision.<n>The proposed C3S3 undergoes rigorous validation on two publicly accessible datasets.
  arXiv  Detail & Related papers  (2025-06-09T02:34:19Z)
• Delta-KNN: Improving Demonstration Selection in In-Context Learning for Alzheimer's Disease Detection [17.171269816416178]
  Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that leads to dementia, and early intervention can greatly benefit from analyzing linguistic abnormalities.<n>In this work, we explore the potential of Large Language Models (LLMs) as health assistants for AD diagnosis from patient-generated text.<n>We introduce Delta-KNN, a novel demonstration selection strategy that enhances ICL performance.
  arXiv  Detail & Related papers  (2025-06-04T01:14:07Z)
• HDC: Hierarchical Distillation for Multi-level Noisy Consistency in Semi-Supervised Fetal Ultrasound Segmentation [2.964206587462833]
  A novel semi-supervised segmentation framework, called HDC, is proposed incorporating adaptive consistency learning with a single-teacher architecture. The framework introduces a hierarchical distillation mechanism with two objectives: Correlation Guidance Loss for aligning feature representations and Mutual Information Loss for stabilizing noisy student learning.
  arXiv  Detail & Related papers  (2025-04-14T04:52:24Z)
• MoEDiff-SR: Mixture of Experts-Guided Diffusion Model for Region-Adaptive MRI Super-Resolution [8.193689534916988]
  MoEDiff-SR is a Mixture of Experts (MoE)-guided diffusion model for region-adaptive MRI Super-Resolution (SR) Unlike conventional diffusion-based SR models that apply a uniform denoising process across the entire image, MoEDiff-SR dynamically selects specialized denoising experts at a fine-grained token level. Experimental results demonstrate that MoEDiff-SR outperforms existing state-of-the-art methods in terms of quantitative image quality metrics, perceptual fidelity, and computational efficiency.
  arXiv  Detail & Related papers  (2025-04-09T22:12:44Z)
• MRGen: Segmentation Data Engine For Underrepresented MRI Modalities [59.61465292965639]
  Training medical image segmentation models for rare yet clinically significant imaging modalities is challenging due to the scarcity of annotated data.<n>This paper investigates leveraging generative models to synthesize training data, to train segmentation models for underrepresented modalities.
  arXiv  Detail & Related papers  (2024-12-04T16:34:22Z)
• LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Models [59.961172635689664]
  "Knowledge Decomposition" aims to improve the performance on specific medical tasks. We propose a novel framework named Low-Rank Knowledge Decomposition (LoRKD) LoRKD explicitly separates gradients from different tasks by incorporating low-rank expert modules and efficient knowledge separation convolution.
  arXiv  Detail & Related papers  (2024-09-29T03:56:21Z)
• Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs [1.5228650878164722]
  Liver cirrhosis, a leading cause of global mortality, requires precise segmentation of ROIs for effective disease monitoring and treatment planning. Existing segmentation models often fail to capture complex feature interactions and generalize across diverse datasets. We propose a novel synergistic theory that leverages complementary latent spaces for enhanced feature interaction modeling.
  arXiv  Detail & Related papers  (2024-08-08T14:41:32Z)
• MEDPSeg: Hierarchical polymorphic multitask learning for the segmentation of ground-glass opacities, consolidation, and pulmonary structures on computed tomography [37.119000111386924]
  MEDPSeg learns from heterogeneous chest CT targets through hierarchical polymorphic multitask learning (HPML) We show PML enabling new state-of-the-art performance for GGO and consolidation segmentation tasks. In addition, MEDPSeg simultaneously performs segmentation of the lung parenchyma, airways, pulmonary artery, and lung lesions, all in a single forward prediction.
  arXiv  Detail & Related papers  (2023-12-04T21:46:39Z)
• End-to-End Breast Cancer Radiotherapy Planning via LMMs with Consistency Embedding [47.360760580820966]
  We present RO-LMM, a comprehensive large multimodal model (LMM) tailored for the field of radiation oncology. This model effectively manages a series of tasks within the clinical workflow, including clinical context summarization, radiation treatment plan suggestion, and plan-guided target volume segmentation. We present a novel Consistency Embedding Fine-Tuning (CEFTune) technique, which boosts LMM's robustness to noisy inputs while preserving the consistency of handling clean inputs.
  arXiv  Detail & Related papers  (2023-11-27T14:49:06Z)
• SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation [4.562266115935329]
  We propose SynergyNet, a novel bottleneck architecture designed to enhance existing encoder-decoder segmentation frameworks. Our experiment on multi-organ segmentation and cardiac datasets demonstrates that SynergyNet outperforms other state of the art methods. Our innovative approach paves the way for enhancing the overall performance and capabilities of deep learning models in the critical domain of medical image analysis.
  arXiv  Detail & Related papers  (2023-10-26T20:13:44Z)
• Diagnosing Alzheimer's Disease using Early-Late Multimodal Data Fusion with Jacobian Maps [1.5501208213584152]
  Alzheimer's disease (AD) is a prevalent and debilitating neurodegenerative disorder impacting a large aging population. We propose an efficient early-late fusion (ELF) approach, which leverages a convolutional neural network for automated feature extraction and random forests. To tackle the challenge of detecting subtle changes in brain volume, we transform images into the Jacobian domain (JD)
  arXiv  Detail & Related papers  (2023-10-25T19:02:57Z)
• Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis [55.03872260158717]
  Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis. Many methods have been proposed to reduce fMRI heterogeneity between source and target domains. But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies. We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
  arXiv  Detail & Related papers  (2023-08-24T01:30:18Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities [60.079782224958414]
  We will present and discuss opportunities and challenges brought by a new deep learning method by AUC (aka underlinebf Deep underlinebf AUC classification)
  arXiv  Detail & Related papers  (2021-11-01T15:31:32Z)
• A Multi-Task Cross-Task Learning Architecture for Ad-hoc Uncertainty Estimation in 3D Cardiac MRI Image Segmentation [0.0]
  We present a Multi-task Cross-task learning consistency approach to enforce the correlation between the pixel-level (segmentation) and the geometric-level (distance map) tasks. Our study further showcases the potential of our model to flag low-quality segmentation from a given model.
  arXiv  Detail & Related papers  (2021-09-16T03:53:24Z)
• Fader Networks for domain adaptation on fMRI: ABIDE-II study [68.5481471934606]
  We use 3D convolutional autoencoders to build the domain irrelevant latent space image representation and demonstrate this method to outperform existing approaches on ABIDE data.
  arXiv  Detail & Related papers  (2020-10-14T16:50:50Z)

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.