Radiology report generation (RRG) is crucial to save the valuable time of radiologists in drafting the report, therefore increasing their work efficiency. Compared to typical methods that directly transfer image captioning technologies to RRG, our approach incorporates organ-wise priors into the report generation. Specifically, in this paper, we propose Organ-aware Diagnosis (OaD) to generate diag...Show More
Semantic segmentation plays a fundamental role in computer vision, underpinning applications such as autonomous driving and scene analysis. Although dual-branch networks have marked advancements in accuracy and processing speed, they falter in the context extraction phase within the low-resolution branch. Traditionally, square pooling is used at this juncture, leading to the oversight of stripe-sh...Show More
This paper introduces an efficient Convolutional Neural Networks (CNN) architecture named DAPSPNet for Real-time semantic segmentation. We propose a novel dual-resolution network, DAPSPNet, and augment it with strip pooling in the multi-scale feature extraction module to extract strip-shaped features more effectively. The convolution kernels have lengths of 5, 9, and 17, with a width of 1. We chos...Show More
Nuclear instance segmentation is a challenging task due to a large number of touching and overlapping nuclei in pathological images. Existing methods cannot effectively recognize the accurate boundary owing to neglecting the relationship between pixels (e.g., direction information). In this paper, we propose a novel Centripetal Direction Net-work (CDNet) for nuclear instance segmentation. Specific...Show More
