Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated data. As a result, various methods which can learn with less/other types of supervision, have been proposed. We review semi-supervised, multiple instance, and transfer learning in medical imaging, both in diagnosis/detection or segmentation tasks. We also discuss connections between these learning scenarios, and opportunities for future research
International audienceHistopathological images are the gold standard for breast cancer diagnosis. Du...
My thesis develops machine learning methods that exploit multimodal clinical data to improve medical...
Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem o...
Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. Whil...
Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. Whil...
Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. Whil...
Deep learning based classification of biomedical images requires expensive manual annotation by expe...
Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis,...
Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical ...
Background Transfer learning is a form of machine learning where a pre-trained model trained on a sp...
Advances in machine learning techniques have been shown to bring benefit for analysing medical image...
Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem o...
BackgroundTransfer learning is a form of machine learning where a pre-trained model trained on a spe...
Background and objectives: Transfer learning is a valuable approach to perform medical image segment...
International audienceHistopathological images are the gold standard for breast cancer diagnosis. Du...
My thesis develops machine learning methods that exploit multimodal clinical data to improve medical...
Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem o...
Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. Whil...
Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. Whil...
Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. Whil...
Deep learning based classification of biomedical images requires expensive manual annotation by expe...
Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis,...
Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical ...
Background Transfer learning is a form of machine learning where a pre-trained model trained on a sp...
Advances in machine learning techniques have been shown to bring benefit for analysing medical image...
Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem o...
BackgroundTransfer learning is a form of machine learning where a pre-trained model trained on a spe...
Background and objectives: Transfer learning is a valuable approach to perform medical image segment...
International audienceHistopathological images are the gold standard for breast cancer diagnosis. Du...
My thesis develops machine learning methods that exploit multimodal clinical data to improve medical...
Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem o...
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