In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …

Ongoing improvements in AI, particularly concerning deep learning techniques, are
assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the …

The missing modality issue is critical but non-trivial to be solved by multi-modal models.
Current methods aiming to handle the missing modality problem in multi-modal tasks, either …

Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …

Despite advances in data augmentation and transfer learning, convolutional neural
networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans …

Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) is desirable to
joint learning of multimodal images. However, in clinical practice, it is not always possible to …

Gliomas are the most common primary brain malignancies, with different degrees of
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …

Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess brain
tumor. Accurately segmenting brain tumor from MR images is the key to clinical diagnostics …

The chest X-ray is one of the most commonly accessible radiological examinations for
screening and diagnosis of many lung diseases. A tremendous number of X-ray imaging …

Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …