Fine-grained attention & knowledge-based collaborative network for diabetic retinopathy grading.

Accurate diabetic retinopathy (DR) grading is crucial for making the proper treatment plan to reduce the damage caused by vision loss. This task is challenging due to the fact that the DR related lesions are often small and subtle in visual differences and intra-class variations. Moreover, relationships between the lesions and the DR levels are complicated.

Delineation of clinical target volume and organs at risk in cervical cancer radiotherapy by deep learning networks.

Purpose: Delineation of the clinical target volume (CTV) and organs-at-risk (OARs) is important in cervical cancer radiotherapy. But it is generally labor-intensive, time-consuming, and subjective. This paper proposes a parallel-path attention fusion network (PPAF-net) to overcome these disadvantages in the delineation task.

Attention-guided multi-scale context aggregation network for multi-modal brain glioma segmentation.

Background: Accurate segmentation of brain glioma is a critical prerequisite for clinical diagnosis, surgical planning and treatment evaluation. In current clinical workflow, physicians typically perform delineation of brain tumor subregions slice-by-slice, which is more susceptible to variabilities in raters and also time-consuming. Besides, even though convolutional neural networks (CNNs) are driving progress, the performance of standard models still have some room for further improvement.

A data mining framework for time series estimation.

Time series estimation techniques are usually employed in biomedical research to derive variables less accessible from a set of related and more accessible variables. These techniques are traditionally built from systems modeling approaches including simulation, blind decovolution, and state estimation. In this work, we define target time series (TTS) and its related time series (RTS) as the output and input of a time series estimation process, respectively.

A comparison of vital signs charted by nurses with automated acquired values using waveform quality indices.

Objective: (1) To investigate if there exist any discrepancies between the values of vital signs charted by nurses and those recorded by bedside monitors for a group of patients admitted for neurocritical care. (2) To investigate possible interpretations of discrepancies by exploring information in the alarm messages and the raw waveform data from monitors.

Methods: Each charted vital sign value was paired with a corresponding value from data collected by an archival program of bedside monitors such that the automatically archived data preceded the charted data and had minimal time lag to the charted value.