Advances in diffusion models for image data augmentation: A review of methods, models, evaluation metrics and future research directions
Image data augmentation constitutes a critical methodology in modern computer vision
tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; …
tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; …
Privacy enhancing and generalizable deep learning with synthetic data for mediastinal neoplasm diagnosis
The success of deep learning (DL) relies heavily on training data from which DL models
encapsulate information. Consequently, the development and deployment of DL models …
encapsulate information. Consequently, the development and deployment of DL models …
[HTML][HTML] Latent diffusion models with image-derived annotations for enhanced ai-assisted cancer diagnosis in histopathology
Artificial Intelligence (AI)-based image analysis has immense potential to support diagnostic
histopathology, including cancer diagnostics. However, developing supervised AI methods …
histopathology, including cancer diagnostics. However, developing supervised AI methods …
Deep Generative Models for 3D Medical Image Synthesis
Deep generative modeling has emerged as a powerful tool for synthesizing realistic medical
images, driving advances in medical image analysis, disease diagnosis, and treatment …
images, driving advances in medical image analysis, disease diagnosis, and treatment …
Cross-attention-based saliency inference for predicting cancer metastasis on whole slide images
Although multiple instance learning (MIL) methods are widely used for automatic tumor
detection on whole slide images (WSI), they suffer from the extreme class imbalance WSIs …
detection on whole slide images (WSI), they suffer from the extreme class imbalance WSIs …
[HTML][HTML] Denoising diffusion probabilistic models for addressing data limitations in chest X-ray classification
EMC Huijben, JPW Pluim… - Informatics in Medicine …, 2024 - Elsevier
Deep learning plays a crucial role in medical imaging analysis, particularly in tasks such as
image classification and segmentation. However, learning from medical imaging datasets …
image classification and segmentation. However, learning from medical imaging datasets …
Image distillation for safe data sharing in histopathology
Histopathology can help clinicians make accurate diagnoses, determine disease prognosis,
and plan appropriate treatment strategies. As deep learning techniques prove successful in …
and plan appropriate treatment strategies. As deep learning techniques prove successful in …
DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Training
Diffusion models (DMs) have emerged as powerful foundation models for a variety of tasks,
with a large focus in synthetic image generation. However, their requirement of large …
with a large focus in synthetic image generation. However, their requirement of large …
Using histopathology latent diffusion models as privacy-preserving dataset augmenters improves downstream classification performance
JM Niehues, G Müller-Franzes, Y Schirris… - Computers in Biology …, 2024 - Elsevier
Latent diffusion models (LDMs) have emerged as a state-of-the-art image generation
method, outperforming previous Generative Adversarial Networks (GANs) in terms of …
method, outperforming previous Generative Adversarial Networks (GANs) in terms of …
Masked conditional diffusion model for enhancing deepfake detection
T Chen, S Yang, S Hu, Z Fang, Y Fu, X Wu… - arXiv preprint arXiv …, 2024 - arxiv.org
Recent studies on deepfake detection have achieved promising results when training and
testing faces are from the same dataset. However, their results severely degrade when …
testing faces are from the same dataset. However, their results severely degrade when …