The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …

Precision Agriculture (PA) promises to boost crop productivity while reducing agricultural
costs and environmental footprints, and therefore is attracting ever-increasing interests in …

Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for
accurate diagnosis and follow-up. Deep learning has a potential to automate this task but …

Learning with the\textit {instance-dependent} label noise is challenging, because it is hard to
model such real-world noise. Note that there are psychological and physiological evidences …

Endomyocardial biopsy (EMB) screening represents the standard of care for detecting
allograft rejections after heart transplant. Manual interpretation of EMBs is affected by …

In label-noise learning, estimating the transition matrix has attracted more and more
attention as the matrix plays an important role in building statistically consistent classifiers …

Classical machine learning implicitly assumes that labels of the training data are sampled
from a clean distribution, which can be too restrictive for real-world scenarios. However …

Automatic medical image segmentation plays a critical role in scientific research and
medical care. Existing high-performance deep learning methods typically rely on large …

We present a critical assessment of the role of transfer learning in training fully convolutional
networks (FCNs) for medical image segmentation. We first show that although transfer …

Skin cancer is a major public health problem that could benefit from computer-aided
diagnosis to reduce the burden of this common disease. Skin lesion segmentation from …