Deep learning for computational cytology: A survey
Computational cytology is a critical, rapid-developing, yet challenging topic in medical
image computing concerned with analyzing digitized cytology images by computer-aided …
image computing concerned with analyzing digitized cytology images by computer-aided …
Deep learning in breast cancer imaging: A decade of progress and future directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
Diffmix: Diffusion model-based data synthesis for nuclei segmentation and classification in imbalanced pathology image datasets
Nuclei segmentation and classification is an important process in pathological image
analysis. Deep learning-based approaches contribute significantly to the enhanced …
analysis. Deep learning-based approaches contribute significantly to the enhanced …
Label-efficient deep learning in medical image analysis: Challenges and future directions
Deep learning has seen rapid growth in recent years and achieved state-of-the-art
performance in a wide range of applications. However, training models typically requires …
performance in a wide range of applications. However, training models typically requires …
Backdoor attack and defense in federated generative adversarial network-based medical image synthesis
R Jin, X Li - Medical Image Analysis, 2023 - Elsevier
Deep Learning-based image synthesis techniques have been applied in healthcare
research for generating medical images to support open research and augment medical …
research for generating medical images to support open research and augment medical …
Nuclei segmentation with point annotations from pathology images via self-supervised learning and co-training
Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology.
Generally, the segmentation performance of fully-supervised learning heavily depends on …
Generally, the segmentation performance of fully-supervised learning heavily depends on …
Data efficient deep learning for medical image analysis: A survey
The rapid evolution of deep learning has significantly advanced the field of medical image
analysis. However, despite these achievements, the further enhancement of deep learning …
analysis. However, despite these achievements, the further enhancement of deep learning …
Domain generalization in computational pathology: survey and guidelines
Deep learning models have exhibited exceptional effectiveness in Computational Pathology
(CPath) by tackling intricate tasks across an array of histology image analysis applications …
(CPath) by tackling intricate tasks across an array of histology image analysis applications …
[HTML][HTML] A systematic review of deep learning data augmentation in medical imaging: Recent advances and future research directions
Data augmentation involves artificially expanding a dataset by applying various
transformations to the existing data. Recent developments in deep learning have advanced …
transformations to the existing data. Recent developments in deep learning have advanced …
LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation
Data augmentation has become a de facto component of deep learning-based medical
image segmentation methods. Most data augmentation techniques used in medical imaging …
image segmentation methods. Most data augmentation techniques used in medical imaging …