Self-supervised learning in medicine and healthcare
The development of medical applications of machine learning has required manual
annotation of data, often by medical experts. Yet, the availability of large-scale unannotated …
annotation of data, often by medical experts. Yet, the availability of large-scale unannotated …
Image augmentation techniques for mammogram analysis
Research in the medical imaging field using deep learning approaches has become
progressively contingent. Scientific findings reveal that supervised deep learning methods' …
progressively contingent. Scientific findings reveal that supervised deep learning methods' …
Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the …
of clinical experts. However, in settings differing from those of the training dataset, the …
Generalizing to unseen domains: A survey on domain generalization
Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …
the same. To this end, a key requirement is to develop models that can generalize to unseen …
Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches
Breast cancer is a significant global health burden, with increasing morbidity and mortality
worldwide. Early screening and accurate diagnosis are crucial for improving prognosis …
worldwide. Early screening and accurate diagnosis are crucial for improving prognosis …
MADG: margin-based adversarial learning for domain generalization
Abstract Domain Generalization (DG) techniques have emerged as a popular approach to
address the challenges of domain shift in Deep Learning (DL), with the goal of generalizing …
address the challenges of domain shift in Deep Learning (DL), with the goal of generalizing …
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 …
Dissecting self-supervised learning methods for surgical computer vision
The field of surgical computer vision has undergone considerable breakthroughs in recent
years with the rising popularity of deep neural network-based methods. However, standard …
years with the rising popularity of deep neural network-based methods. However, standard …
[HTML][HTML] Domain generalization in deep learning based mass detection in mammography: A large-scale multi-center study
Computer-aided detection systems based on deep learning have shown great potential in
breast cancer detection. However, the lack of domain generalization of artificial neural …
breast cancer detection. However, the lack of domain generalization of artificial neural …
AI in breast cancer imaging: A survey of different applications
Breast cancer was the most diagnosed cancer in 2020. Several thousand women continue
to die from this disease. A better and earlier diagnosis may be of great importance to …
to die from this disease. A better and earlier diagnosis may be of great importance to …