Brain tumors have shown extreme mortality and increasing incidence during recent years,
which bring enormous challenges for the timely diagnosis and effective treatment of brain …

Automatic segmentation of medical images plays an important role in the diagnosis of
diseases. On single-modal data, convolutional neural networks have demonstrated …

Multi-modality medical data provide complementary information, and hence have been
widely explored for computer-aided AD diagnosis. However, the research is hindered by the …

In medical vision, different imaging modalities provide complementary information. However,
in practice, not all modalities may be available during inference or even training. Previous …

Methods In in the PubMed and Google Scholar databases, the keywords “multi-
modal”,“deep learning”, and “tumor segmentation” were used to systematically search …

Modern deep learning techniques on automatic multimodal medical diagnosis rely on
massive expert annotations, which is time-consuming and prohibitive. Recent masked …

Accurate segmentation of brain tumors is of critical importance in clinical assessment and
treatment planning, which requires multiple MR modalities providing complementary …

Gliomas are the most common and aggressive form of all brain tumors, with medial survival
rates of less than two years for the highest grade. While accurate and reproducible …

Accurately identifying tumors from MRI scans is of the utmost importance for clinical
diagnostics and when making plans regarding brain tumor treatment. However, manual …

Cortical cataract, a common type of cataract, is particularly difficult to be diagnosed
automatically due to the complex features of the lesions. Recently, many methods based on …