<p>Melanoma Classification - a 10,000$ competition. For the 2020 Melanoma Classification competition hosted by kaggle, 33,126 images were made available for training (of which 2% were melanomas), and an additional 10,982 were used for final ranking of the 3,308 teams who entered the competition with eyes on the 10,000$ prize. The task was simple: provide a probability for melanoma (deadly skin cancer) ...

Development, validation and comparison of machine learning methods require access to data, sometimes lots of data. Within health applications, data sharing can be restricted due to patient privacy, and the few publicly available data sets become even more valuable for the machine learning community. One such type of data are H&E whole slide images (WSI), which are stained tumour tissue, used in ...

Melanoma is the deadliest form of skin cancer, and early detection is crucial for patient survival. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic/manual segmentation masks ...

<p>The old “Lofoten Post” building sits at the end of the dock in Svolvær as an empty shell, ready for the winter storms to seal its destiny. Everything that reminds of newspaper production is stripped from the building, only dents from the editor’s desk can still be seen on the floor. Wait! A stack of newspapers, filled with tables of fishery, trade, crimes, and everything else that transforms small ...

Multi-instance (MI) learning is a branch of machine learning, where each object (bag) consists of multiple feature vectors (instances)—for example, an image consisting of multiple patches and their corresponding feature vectors. In MI classification, each bag in the training set has a class label, but the instances are unlabeled. The instances are most commonly regarded as a set of points in a ...