Unsupervised image-to-image translation is an inherently ill-posed problem. This paper uses linear encoder-decoder architectures for unsupervised image to image translation. (via @reckoningdev)
Since deep neural networks are over-parameterized, they can memorize noisy examples. We address such memorizing issue in the presence of annotation noise. (via @reckoningdev)
Dense object detectors rely on the sliding-window paradigm that predicts the object over a regular grid of image. Meanwhile, the feature maps on the point of the grid are adopted to generate… (via @reckoningdev)
Those of us who see the great potential of artificial intelligence in radiology are eager to assure that AI systems work to the benefit of all of our patients. To do so, we must be aware of possibilities for error. In quality management, a latent error is a failure that is “waiting to happen,” often due to an oversight in design or execution.