Diabetic retinopathy (DR) is a leading cause of permanent blindness among the working-age people. Automatic DR grading can help ophthalmologists make timely treatment for patients. However, the existing grading methods are usually trained with high resolution (HR) fundus images, such that the grading performance decreases a lot given low resolution (LR) images, which are common in clinic. In this paper, we mainly focus on DR grading with LR fundus images. According to our analysis on the DR task, we find that: 1) image super-resolution (ISR) can boost the performance of both DR grading and lesion segmentation; 2) the lesion segmentation regions of fundus images are highly consistent with pathological regions for DR grading. Based on our findings, we propose a convolutional neural network (CNN)-based method for joint learning of multi-level tasks for DR grading, called DeepMT-DR, which can simultaneously handle the low-level task of ISR, the mid-level task of lesion segmentation and the high-level task of disease severity classification on LR fundus images. Moreover, a novel task-aware loss is developed to encourage ISR to focus on the pathological regions for its subsequent tasks: lesion segmentation and DR grading. Extensive experimental results show that our DeepMT-DR method significantly outperforms other state-of-the-art methods for DR grading over three datasets. In addition, our method achieves comparable performance in two auxiliary tasks of ISR and lesion segmentation.

Mismatch negativity (MMN) has been consistently found deficit in schizophrenia, which was considered as a promising biomarker for assessing the impairments in pre-attentive auditory processing. However, the functional connectivity between brain regions based on MMN is not clear. This study provides an in-depth investigation in brain functional connectivity during MMN process among patients with first-episode schizophrenia (FESZ), chronic schizophrenia (CSZ) and healthy control (HC). Electroencephalography (EEG) data of 128 channels is recorded during frequency and duration MMN in 40 FESZ, 40 CSZ patients and 40 matched HC subjects. We reconstruct the cortical endogenous electrical activity from EEG recordings using exact low-resolution electromagnetic tomography and build functional brain networks based on source-level EEG data. Then, graph-theoretic features are extracted from the brain networks with the support vector machine (SVM) to classify FESZ, CSZ and HC groups, since the SVM has good generalization ability and robustness as a universally applicable nonlinear classifier. Furthermore, we introduce the graph neural network (GNN) model to directly learn for the network topology of brain network. Compared to HC, the damaged brain areas of CSZ are more extensive than FESZ, and the damaged area involved the auditory cortex. These results demonstrate the heterogeneity of the impacts of schizophrenia for different disease courses and the association between MMN and the auditory cortex. More importantly, the GNN classification results are significantly better than those of SVM, and hence the EEG-based GNN model of brain networks provides an effective method for discriminating among FESZ, CSZ and HC groups.

Semantic segmentation of medical images provides an important cornerstone for subsequent tasks of image analysis and understanding. With rapid advancements in deep learning methods, conventional U-Net segmentation networks have been applied in many fields. Based on exploratory experiments, features at multiple scales have been found to be of great importance for the segmentation of medical images. In this paper, we propose a scale-attention deep learning network (SA-Net), which extracts features of different scales in a residual module and uses an attention module to enforce the scale-attention capability. SA-Net can better learn the multi-scale features and achieve more accurate segmentation for different medical image. In addition, this work validates the proposed method across multiple datasets. The experiment results show SA-Net achieves excellent performances in the applications of vessel detection in retinal images, lung segmentation, artery/vein(A/V) classification in retinal images and blastocyst segmentation. To facilitate SA-Net utilization by the scientific community, the code implementation will be made publicly available.