Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available datasets of dermatoscopic images. We tackle this problem by releasing the HAM10000 (“Human Against Machine with 10000 training images”) dataset. We collected dermatoscopic images from different populations acquired and stored by different modalities. Giv...

As one kind of skin cancer, melanoma is very dangerous. Dermoscopy based early detection and recognization strategy is critical for melanoma therapy. However, well-trained dermatologists dominant the diagnostic accuracy. In order to solve this problem, many effort focus on developing automatic image analysis systems. Here we report a novel strategy based on deep learning technique, and achieve ...

With the increasing incidence of severe skin diseases, such as cancer, endoscopic medical imaging has become urgent for revealing internal and hidden tissues under skin. Diagnostic information to help doctors make an accurate diagnosis is provided by endoscopy devices. Nonetheless, most diseases have similar features, which it challenging dermatologists diagnose patients accurately. Therefore, ...

We present a method for skin lesion segmentation for the ISIC 2017 Skin Lesion Segmentation Challenge. Our approach is based on a Fully Convolutional Neural Network architecture which is trained end to end, from scratch, on a small dataset. Our semantic segmentation architecture utilizes several recent innovations in deep learning particularly in the combined use of (i) atrous convolutions to i...

Melanoma is the most lethal malignant tumour, and its prevalence increasing. Early detection diagnosis of skin cancer can alert patients to manage precautions dramatically improve lives people. Recently, deep learning has grown increasingly popular in extraction categorization features for effective prediction. A model learns co-adapts representations from training data point where it fails per...

Edoardo Baldini,1,2 Andreas Mann,1 Benjamin P. P. Mallett,3 Christopher Arrell,2 Frank van Mourik,2 Thomas Wolf,4 Dragan Mihailovic,5 Jeffrey L. Tallon,6 Christian Bernhard,3 José Lorenzana,7 and Fabrizio Carbone1 1Laboratory for Ultrafast Microscopy and Electron Scattering, IPHYS, EPFL, CH-1015 Lausanne, Switzerland 2Laboratory of Ultrafast Spectroscopy, ISIC, EPFL, CH-1015 Lausanne, Switzerla...

We use a pretrained fully convolutional neural network to detect clinical dermoscopic features from dermoscopy skin lesion images. We reformulate the superpixel classification task as an image segmentation problem, and extend a neural network architecture originally designed for image classification to detect dermoscopic features. Specifically, we interpolate the feature maps from several layer...