Classification

The paper proposes using Generative Adversarial Networks (GANs) to augment the dataset with high quality synthetic liver lesion images in order to improve the CNN classification performance for medical image classification. The authors use limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The liver lesions vary considerably in shape, contrast and size, and also present intra-class variability. ...

The Dataset The paper uses a new dermatologist-labelled dataset of 129,450 clinical images, which also includes 3,374 dermoscopic images. These images come from 18 different clinician-curated, open-access online repositories, as well as from clinical data from Stanford University Medical Center, and belong to 2,032 diseases. This data is split into 127,463 training and validation images, and 1,942 biopsy-labelled test images. ...

This paper proposes a high level latent and shared feature representation from neuroimaging modalities (MRI and PET) via deep learning for the diagnosis of Alzheimer’s Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI). In contrast to the previous works where the multimodal features were combined by concatenating into long vectors or transforming into a high dimensional kernel space, the authors propose using a Deep Boltzmann Machine (DBM) to find a latent hierarchical representation from a 3D patch, and then come up with a method for “a joint feature representation from the paired patches of MRI and PET with a multimodal DBM.” ...