Assessing Intra-class Diversity and Quality of Synthetically Generated Images in a Biomedical and Non-biomedical Setting

• URL: http://arxiv.org/abs/2308.02505v1
• Date: Sun, 23 Jul 2023 16:39:18 GMT
• Title: Assessing Intra-class Diversity and Quality of Synthetically Generated Images in a Biomedical and Non-biomedical Setting
• Authors: Muhammad Muneeb Saad, Mubashir Husain Rehmani, and Ruairi O'Reilly
• Abstract summary: Generative Adversarial Networks (GANs) are increasingly being relied upon for data augmentation tasks. The diversity and quality of synthetic images are evaluated using different sample sizes. Results demonstrate that the metrics scores for diversity and quality vary significantly across biomedical-to-biomedical and biomedical-to-non-biomedical imaging modalities.
• Score: 0.6308539010172307
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In biomedical image analysis, data imbalance is common across several imaging modalities. Data augmentation is one of the key solutions in addressing this limitation. Generative Adversarial Networks (GANs) are increasingly being relied upon for data augmentation tasks. Biomedical image features are sensitive to evaluating the efficacy of synthetic images. These features can have a significant impact on metric scores when evaluating synthetic images across different biomedical imaging modalities. Synthetically generated images can be evaluated by comparing the diversity and quality of real images. Multi-scale Structural Similarity Index Measure and Cosine Distance are used to evaluate intra-class diversity, while Frechet Inception Distance is used to evaluate the quality of synthetic images. Assessing these metrics for biomedical and non-biomedical imaging is important to investigate an informed strategy in evaluating the diversity and quality of synthetic images. In this work, an empirical assessment of these metrics is conducted for the Deep Convolutional GAN in a biomedical and non-biomedical setting. The diversity and quality of synthetic images are evaluated using different sample sizes. This research intends to investigate the variance in diversity and quality across biomedical and non-biomedical imaging modalities. Results demonstrate that the metrics scores for diversity and quality vary significantly across biomedical-to-biomedical and biomedical-to-non-biomedical imaging modalities.

Related papers

• BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once [58.41069132627823]
  holistic image analysis comprises subtasks such as segmentation, detection, and recognition of relevant objects. Here, we propose BiomedParse, a biomedical foundation model for imaging parsing that can jointly conduct segmentation, detection, and recognition for 82 object types across 9 imaging modalities. Through joint learning, we can improve accuracy for individual tasks and enable novel applications such as segmenting all relevant objects in a noisy image through a text prompt.
  arXiv  Detail & Related papers  (2024-05-21T17:54:06Z)
• QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge [93.61262892578067]
  Uncertainty in medical image segmentation tasks, especially inter-rater variability, presents a significant challenge. This variability directly impacts the development and evaluation of automated segmentation algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ)
  arXiv  Detail & Related papers  (2024-03-19T17:57:24Z)
• A Self-attention Guided Multi-scale Gradient GAN for Diversified X-ray Image Synthesis [0.6308539010172307]
  Generative Adversarial Networks (GANs) are utilized to address the data limitation problem via the generation of synthetic images. Training challenges such as mode collapse, non-convergence, and instability degrade a GAN's performance in synthesizing diversified and high-quality images. This work proposes an attention-guided multi-scale gradient GAN architecture to model the relationship between long-range dependencies of biomedical image features.
  arXiv  Detail & Related papers  (2022-10-09T13:17:17Z)
• Evaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagery [0.07499722271664144]
  Publicly available diabetic retinopathy (DR) datasets are imbalanced, containing limited numbers of images with DR. The imbalance can be addressed using Geneversarative Adrial Networks (GANs) to augment the datasets with synthetic images. To evaluate the quality and diversity of synthetic images, several evaluation metrics, such as Multi-Scale Structural Similarity Index (MS-SSIM), Cosine Distance (CD), and Fr't Inception Distance (FID) are used.
  arXiv  Detail & Related papers  (2022-08-10T23:50:01Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Computer-Assisted Analysis of Biomedical Images [1.0116577992023341]
  This thesis aims at proposing novel and advanced computer-assisted methods for biomedical image analysis. The ultimate goal of these research studies is to gain clinically and biologically useful insights that can guide differential diagnosis and therapies.
  arXiv  Detail & Related papers  (2021-06-04T21:59:48Z)
• Diffusion-Weighted Magnetic Resonance Brain Images Generation with Generative Adversarial Networks and Variational Autoencoders: A Comparison Study [55.78588835407174]
  We show that high quality, diverse and realistic-looking diffusion-weighted magnetic resonance images can be synthesized using deep generative models. We present two networks, the Introspective Variational Autoencoder and the Style-Based GAN, that qualify for data augmentation in the medical field.
  arXiv  Detail & Related papers  (2020-06-24T18:00:01Z)
• DAN: A Deformation-Aware Network for Consecutive Biomedical Image Interpolation [10.856845408856588]
  This paper introduces a deformation-aware network to synthesize each pixel in accordance with the continuity of biological tissue. We present an adaptive style-balance loss to take the style differences of consecutive biomedical images such as blur and noise into consideration. Guided by the deformation-aware module, we synthesize each pixel from a global domain adaptively which further improves the performance of pixel synthesis.
  arXiv  Detail & Related papers  (2020-04-23T11:14:44Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)
• Automatic image-based identification and biomass estimation of invertebrates [70.08255822611812]
  Time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed. We propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology. We use state-of-the-art Resnet-50 and InceptionV3 CNNs for the classification task.
  arXiv  Detail & Related papers  (2020-02-05T21:38:57Z)

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.