They might significantly deteriorate the performance of convolutional neural networks (CNNs), because CNNs are easily overfitted on corrupted labels.
An open question in deep clustering is how to understand what in the image is creating the cluster assignments.
Automatic pancreas segmentation in radiology images, eg., computed tomography (CT) and magnetic resonance imaging (MRI), is frequently required by computer-aided screening, diagnosis, and quantitative assessment.
Then we review their recent applications in medical image analysis and point out limitations, with the goal to light some potential directions in medical image analysis.
The output layer of this network module is then connected to recurrent layers and can be fine-tuned for contextual learning, in an end-to-end manner.
In this paper, we propose MDNet to establish a direct multimodal mapping between medical images and diagnostic reports that can read images, generate diagnostic reports, retrieve images by symptom descriptions, and visualize attention, to provide justifications of the network diagnosis process.
In this paper, we propose a simple but effective method for fast image segmentation.
In this paper, we investigate the usage of semi-supervised learning (SSL) to obtain competitive detection accuracy with very limited training data (three labeled images).