Lanfen Lin

TL;DR: This work proposes the first 3D liver vessel segmentation network using deep learning that uses a multi-pathways deep learning network and demonstrates impressive performance in comparison with the state-of-the-art methods.

TL;DR: A semi-supervised adversarial learning model with Deep Atlas Prior (DAP), which is based on the probability atlas of organ (liver) and contains prior information such as the shape and position, is proposed to improve the accuracy of liver segmentation in CT images.

TL;DR: This study investigates an improved codebook model for the fined-grained medical image representation with the following three advantages: instead of SIFT, the local patch (structure) is exploited as the local descriptor, which can retain all detailed information and is more suitable for the fine-graining medical image applications.

TL;DR: A framework based on deep learning is proposed, called ResGL-BDLSTM, which combines a residual deep neural network with global and local pathways with a bi-directional long short-term memory model for the task of focal liver lesions classification in multi-phase CT images.

TL;DR: This chapter introduces fundamentals of deep convolutional neural networks for image classification and then introduces an application of deep learning to classification of focal liver lesions on multi-phase CT images.