PanoGAN A Deep Generative Model for Panoramic Dental Radiographs

• URL: http://arxiv.org/abs/2507.21200v1
• Date: Mon, 28 Jul 2025 10:55:44 GMT
• Title: PanoGAN A Deep Generative Model for Panoramic Dental Radiographs
• Abstract summary: This paper presents the development of a generative adversarial network (GAN) for synthesizing dental panoramic radiographs.<n>Although exploratory in nature, the study aims to address the scarcity of data in dental research and education.
• Score: 0.7037008937757394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents the development of a generative adversarial network (GAN) for synthesizing dental panoramic radiographs. Although exploratory in nature, the study aims to address the scarcity of data in dental research and education. We trained a deep convolutional GAN (DCGAN) using a Wasserstein loss with gradient penalty (WGANGP) on a dataset of 2322 radiographs of varying quality. The focus was on the dentoalveolar regions, other anatomical structures were cropped out. Extensive preprocessing and data cleaning were performed to standardize the inputs while preserving anatomical variability. We explored four candidate models by varying critic iterations, feature depth, and the use of denoising prior to training. A clinical expert evaluated the generated radiographs based on anatomical visibility and realism, using a 5-point scale (1 very poor 5 excellent). Most images showed moderate anatomical depiction, although some were degraded by artifacts. A trade-off was observed the model trained on non-denoised data yielded finer details especially in structures like the mandibular canal and trabecular bone, while a model trained on denoised data offered superior overall image clarity and sharpness. These findings provide a foundation for future work on GAN-based methods in dental imaging.

Related papers

• Reconstruction-Shift Discrimination via Mask-Guided Latent Diffusion for Medical Anomaly Detection [58.92871907037662]
  Unsupervised medical anomaly detection learns normal anatomical patterns from healthy training images and identifies deviations at test time.<n>Reconstruction-based and diffusion-based methods commonly use the difference between an input image and its reconstruction as anomaly evidence.<n>We propose discriminative mask-guided diffusion (DMD), a medical anomaly detection framework that complements residual-based localization with reconstruction-shift discrimination.
  arXiv  Detail & Related papers  (2026-08-01T04:56:39Z)
• Caries DETR: Tooth Structure-aware Prior and Lesion-aware Dynamic Loss Refinement for DETR Based Caries Detection [38.87257255907775]
  We propose Caries-DETR, a specialized Transformer framework for caries detection in intraoral images.<n>A Tooth Structure-aware Query Initialization (TSQI) is designed, leveraging large-scale intraoral photograph pre-training.<n>We also design a Lesion-aware Dynamic Loss Refinement (LDLR) to implement quality-driven hard mining.
  arXiv  Detail & Related papers  (2026-04-26T14:02:41Z)
• Weighted Knowledge Distillation for Semi-Supervised Segmentation of Maxillary Sinus in Panoramic X-ray Images [18.248993883711496]
  We propose a semi-supervised framework for accurate segmentation of maxillary sinus in panoramic X-ray images.<n>Knowledge distillation is utilized to train a student model with reliable structural information distilled from a teacher model.<n>ScientistCycle-GAN is a refinement network based on unpaired image-to-image translation.
  arXiv  Detail & Related papers  (2026-04-22T06:00:14Z)
• DiffusionXRay: A Diffusion and GAN-Based Approach for Enhancing Digitally Reconstructed Chest Radiographs [0.13048920509133807]
  We introduce DiffusionXRay, a novel image restoration pipeline for Chest X-ray images.<n>We train a DDPM-based model on paired low-quality and high-quality images, enabling it to learn the nuances of X-ray image restoration.<n>Our method demonstrates promising results in enhancing image clarity, contrast, and overall diagnostic value of chest X-rays.
  arXiv  Detail & Related papers  (2026-03-02T10:14:50Z)
• Adapting Foundation Model for Dental Caries Detection with Dual-View Co-Training [53.77904429789069]
  We present Attention-TNet, a novel Dual-View Co-Training network for accurate dental caries detection.<n>OurTNet starts with employing automated tooth detection to establish two complementary views: a global view from panoramic X-ray images and a local view from cropped tooth images.<n>To effectively integrate information from both views, we introduce a Gated Cross-View module.
  arXiv  Detail & Related papers  (2025-08-28T14:13:26Z)
• 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)
• ADNP-15: An Open-Source Histopathological Dataset for Neuritic Plaque Segmentation in Human Brain Whole Slide Images with Frequency Domain Image Enhancement for Stain Normalization [7.9997900784270035]
  Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by amyloid-beta plaques and tau neurofibrillary tangles.<n>The identification and segmentation of these lesions are crucial for understanding AD progression.<n>Deep learning has emerged as a powerful tool for pathology image segmentation.
  arXiv  Detail & Related papers  (2025-05-08T08:25:44Z)
• Exploring the Efficacy of Partial Denoising Using Bit Plane Slicing for Enhanced Fracture Identification: A Comparative Study of Deep Learning-Based Approaches and Handcrafted Feature Extraction Techniques [0.0]
  Bit plane slicing enhances medical images by reducing noise interference and extracting informative features.<n>This research explores partial denoising techniques to provide solutions for improved fracture analysis.<n>The outcomes of this research provide valuable insights into the development of efficient preprocessing.
  arXiv  Detail & Related papers  (2025-03-21T10:39:21Z)
• Semi-supervised classification of dental conditions in panoramic radiographs using large language model and instance segmentation: A real-world dataset evaluation [6.041146512190833]
  A semi-supervised learning framework is proposed to classify thirteen dental conditions on panoramic radiographs. The solution demonstrated an accuracy level comparable to that of a junior specialist.
  arXiv  Detail & Related papers  (2024-06-25T19:56:12Z)
• Self-supervised vision-langage alignment of deep learning representations for bone X-rays analysis [53.809054774037214]
  This paper proposes leveraging vision-language pretraining on bone X-rays paired with French reports. It is the first study to integrate French reports to shape the embedding space devoted to bone X-Rays representations.
  arXiv  Detail & Related papers  (2024-05-14T19:53:20Z)
• Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation [7.7545714516743045]
  We propose an end-to-end framework called VerseDiff-UNet, which leverages the denoising diffusion probabilistic model (DDPM) Our approach integrates the diffusion model into a standard U-shaped architecture. We evaluate our method on a single dataset of spine images acquired through X-ray imaging.
  arXiv  Detail & Related papers  (2023-09-12T03:05:00Z)
• Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images [39.94162291765236]
  We present a weakly supervised method to generate a healthy version of a diseased image and then use it to obtain a pixel-wise anomaly map. We employ a diffusion model trained on healthy samples and combine Denoising Diffusion Probabilistic Model (DDPM) and Denoising Implicit Model (DDIM) at each step of the sampling process.
  arXiv  Detail & Related papers  (2023-08-03T21:56:50Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• Orientation-Shared Convolution Representation for CT Metal Artifact Learning [63.67718355820655]
  During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts. Existing deep-learning-based methods have gained promising reconstruction performance. We propose an orientation-shared convolution representation strategy to adapt the physical prior structures of artifacts.
  arXiv  Detail & Related papers  (2022-12-26T13:56:12Z)
• Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders [50.689585476660554]
  We propose a new fine-tuning strategy that includes positive-pair loss relaxation and random sentence sampling. Our approach consistently improves overall zero-shot pathology classification across four chest X-ray datasets and three pre-trained models.
  arXiv  Detail & Related papers  (2022-12-14T06:04:18Z)
• Metal Artifact Reduction with Intra-Oral Scan Data for 3D Low Dose Maxillofacial CBCT Modeling [0.7444835592104696]
  A two-stage metal artifact reduction method is proposed for accurate 3D low-dose maxillofacial CBCT modeling. In the first stage, an image-to-image deep learning network is employed to mitigate metal-related artifacts. In the second stage, a 3D maxillofacial model is constructed by segmenting the bones from the dental CBCT image corrected.
  arXiv  Detail & Related papers  (2022-02-08T00:24:41Z)
• SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection [76.01333073259677]
  We propose the use of Space-aware Memory Queues for In-painting and Detecting anomalies from radiography images (abbreviated as SQUID) We show that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns; and in the inference, it can identify anomalies (unseen/modified patterns) in the image.
  arXiv  Detail & Related papers  (2021-11-26T13:47:34Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)

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.