A modified U-net with graph representation for dose prediction in esophageal cancer radiotherapy plans.

The manual design of esophageal cancer radiotherapy plan is time-consuming and labor-intensive. Automatic planning (AP) is prevalent nowadays to increase physicists' work efficiency. Because of the intuitiveness of dose distribution in AP evaluation, obtaining reasonable dose prediction provides effective guarantees to generate a satisfactory AP. Existing fully convolutional network-based methods for predicting dose distribution in esophageal cancer radiotherapy plans often capture features in a limited receptive field. Additionally, the correlations between voxel pairs are often ignored. This work modifies the U-net architecture and exploits graph convolution to capture long-range information for dose prediction in esophageal cancer plans. Meanwhile, attention mechanism gets correlations between planning target volume (PTV) and organs at risk, and adaptively learns their feature weights. Finally, a novel loss function that considers features between voxel pairs is used to highlight the predictions. 152 subjects with prescription doses of 50 Gy or 60 Gy are collected in this study. The mean absolute error and standard deviation of conformity index, homogeneity index, and max dose for PTV achieved by the proposed method are 0.036 ± 0.030, 0.036 ± 0.027, and 0.930 ± 1.162, respectively, which outperform other state-of-the-art models. The superior performance demonstrates that our proposed method has great potential for AP generation.

Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears.

Technical advancements significantly improve earlier diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence assistive diagnostic solution, AIATBS, to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria. We train AIATBS with >81,000 retrospective samples. It integrates YOLOv3 for target detection, Xception and Patch-based models to boost target classification, and U-net for nucleus segmentation. We integrate XGBoost and a logical decision tree with these models to optimize the parameters given by the learning process, and we develop a complete cervical liquid-based cytology smear TBS diagnostic system which also includes a quality control solution. We validate the optimized system with >34,000 multicenter prospective samples and achieve better sensitivity compared to senior cytologists, yet retain high specificity while achieving a speed of <180s/slide. Our system is adaptive to sample preparation using different standards, staining protocols and scanners.

The Role of Phonological Processing in Semantic Access of Chinese Characters: A Near-Infrared Spectroscopy Study.

The Stroop task was used to investigate the role of phonological processing in semantic access for written Chinese language. Fourteen children were recruited to perform the Stroop task, using color characters, their homophones and neutral characters as stimuli. Near-infrared spectroscopy (NIRS) was used to measure the brain activation in the prefrontal cortex (PFC) during the task. In view of better sensitivity, oxy-hemoglobin was chosen to indicate the task activation. In behavioral performance, there was a significant classical Stroop interference effect as indexed by longer response time and higher error rate for the color task than the neutral task, whereas there was no evident interference effect for the color homophones. The NIRS data agreed with the behavioral data, and showed a significant Stroop effect only for the color characters in the bilateral PFC. These results suggested that phonology may not play an important role in semantic activation of Chinese characters for children.