Mingxia Liu

TL;DR: A hierarchical fully convolutional network (H-FCN) is proposed to automatically identify discriminative local patches and regions in the whole brain sMRI, upon which multi-scale feature representations are then jointly learned and fused to construct hierarchical classification models for AD diagnosis.

TL;DR: This paper adopts a data‐driven learning approach to discover disease‐related anatomical landmarks in the brain MR images, along with their nearby image patches, and learns an end‐to‐end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks.

TL;DR: This work identifies the discriminative anatomical landmarks from MR images in a data-driven manner, and proposes a deep multi-task multi-channel convolutional neural network for joint classification and regression, using MRI data and demographic information of subjects.

TL;DR: In this article, a survey of domain adaptation methods for medical image analysis is presented, and the authors categorize the existing methods into shallow and deep models, and each of them is further divided into supervised, semi-supervised and unsupervised methods.

TL;DR: A two-stage task-oriented deep learning method to detect large-scale anatomical landmarks simultaneously in real time, using limited training data, consisting of two deep convolutional neural networks, with each focusing on one specific task.