Improving Deep Learning-based Automatic Cranial Defect Reconstruction by Heavy Data Augmentation: From Image Registration to Latent Diffusion Models

• URL: http://arxiv.org/abs/2406.06372v1
• Date: Mon, 10 Jun 2024 15:34:23 GMT
• Title: Improving Deep Learning-based Automatic Cranial Defect Reconstruction by Heavy Data Augmentation: From Image Registration to Latent Diffusion Models
• Authors: Marek Wodzinski, Kamil Kwarciak, Mateusz Daniol, Daria Hemmerling,
• Abstract summary: The work is a considerable contribution to the field of artificial intelligence in the automatic modeling of personalized cranial implants. We show that the use of heavy data augmentation significantly increases both the quantitative and qualitative outcomes. We also show that the synthetically augmented network successfully reconstructs real clinical defects.
• Score: 0.2911706166691895
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Modeling and manufacturing of personalized cranial implants are important research areas that may decrease the waiting time for patients suffering from cranial damage. The modeling of personalized implants may be partially automated by the use of deep learning-based methods. However, this task suffers from difficulties with generalizability into data from previously unseen distributions that make it difficult to use the research outcomes in real clinical settings. Due to difficulties with acquiring ground-truth annotations, different techniques to improve the heterogeneity of datasets used for training the deep networks have to be considered and introduced. In this work, we present a large-scale study of several augmentation techniques, varying from classical geometric transformations, image registration, variational autoencoders, and generative adversarial networks, to the most recent advances in latent diffusion models. We show that the use of heavy data augmentation significantly increases both the quantitative and qualitative outcomes, resulting in an average Dice Score above 0.94 for the SkullBreak and above 0.96 for the SkullFix datasets. Moreover, we show that the synthetically augmented network successfully reconstructs real clinical defects. The work is a considerable contribution to the field of artificial intelligence in the automatic modeling of personalized cranial implants.

Related papers

• TSynD: Targeted Synthetic Data Generation for Enhanced Medical Image Classification [0.011037620731410175]
  This work aims to guide the generative model to synthesize data with high uncertainty. We alter the feature space of the autoencoder through an optimization process. We improve the robustness against test time data augmentations and adversarial attacks on several classifications tasks.
  arXiv  Detail & Related papers  (2024-06-25T11:38:46Z)
• Simulating Realistic Post-Stroke Reaching Kinematics with Generative Adversarial Networks [0.0]
  This study employs Conditional Generative Adversarial Networks (cGANs) to create synthetic kinematic data from a publicly available dataset. By training deep learning models on both synthetic and experimental data, we achieved a substantial enhancement in task classification accuracy.
  arXiv  Detail & Related papers  (2024-06-12T15:51:00Z)
• DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
  Current perceptive models heavily depend on resource-intensive datasets. We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability. Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
  arXiv  Detail & Related papers  (2024-03-20T04:58:03Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Deep Generative Networks for Heterogeneous Augmentation of Cranial Defects [0.15720523553334917]
  We show that it is possible to generate dozens of thousands of defective skulls with compatible defects. The generated skulls may improve the automatic design of personalized cranial implants for real medical cases.
  arXiv  Detail & Related papers  (2023-08-09T11:29:16Z)
• Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images [2.5075774184834803]
  Deep neural networks still rely on large amounts of training data to avoid overfitting. Labeled training data for real-world applications such as healthcare is limited and difficult to access. We build upon the emerging success of text-to-image diffusion probabilistic models in augmenting the training samples of our macroscopic skin disease dataset.
  arXiv  Detail & Related papers  (2023-01-12T04:22:23Z)
• An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design [55.62660894625669]
  Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable. Recent developments in machine learning have provided alternative solutions in replacing the time-consuming lithography simulations with deep neural networks. We propose a litho-aware data augmentation framework to resolve the dilemma of limited data and improve the machine learning model performance.
  arXiv  Detail & Related papers  (2022-10-27T20:53:39Z)
• Ultrasound Signal Processing: From Models to Deep Learning [64.56774869055826]
  Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Deep learning based methods, which are optimized in a data-driven fashion, have gained popularity. A relatively new paradigm combines the power of the two: leveraging data-driven deep learning, as well as exploiting domain knowledge.
  arXiv  Detail & Related papers  (2022-04-09T13:04:36Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• "Name that manufacturer". Relating image acquisition bias with task complexity when training deep learning models: experiments on head CT [0.0]
  We analyze how the distribution of scanner manufacturers in a dataset can contribute to the overall bias of deep learning models. We demonstrate that CNNs can learn to distinguish the imaging scanner manufacturer and that this bias can substantially impact model performance.
  arXiv  Detail & Related papers  (2020-08-19T16:05:58Z)

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.