Advanced Multi-Microscopic Views Cell Semi-supervised Segmentation

Although deep learning (DL) shows powerful potential in cell segmentation tasks, it suffers from poor generalization as DL-based methods originally simplified cell segmentation in detecting cell membrane boundary, lacking prominent cellular structures to position overall differentiating. Moreover, the scarcity of annotated cell images limits the performance of DL models. Segmentation limitations of a single category of cell make massive practice difficult, much less, with varied modalities. In this paper, we introduce a novel semi-supervised cell segmentation method called Multi-Microscopic-view Cell semi-supervised Segmentation (MMCS), which can train cell segmentation models utilizing less labeled multi-posture cell images with different microscopy well. Technically, MMCS consists of Nucleus-assisted global recognition, Self-adaptive diameter filter, and Temporal-ensembling models. Nucleus-assisted global recognition adds additional cell nucleus channel to improve the global distinguishing performance of fuzzy cell membrane boundaries even when cells aggregate. Besides, self-adapted cell diameter filter can help separate multi-resolution cells with different morphology properly. It further leverages the temporal-ensembling models to improve the semi-supervised training process, achieving effective training with less labeled data. Additionally, optimizing the weight of unlabeled loss contributed to total loss also improve the model performance. Evaluated on the Tuning Set of NeurIPS 2022 Cell Segmentation Challenge (NeurIPS CellSeg), MMCS achieves an F1-score of 0.8239 and the running time for all cases is within the time tolerance.

Multiform Fonts-to-Fonts Translation via Style and Content Disentangled Representations of Chinese Character

This paper mainly discusses the generation of personalized fonts as the problem of image style transfer. The main purpose of this paper is to design a network framework that can extract and recombine the content and style of the characters. These attempts can be used to synthesize the entire set of fonts with only a small amount of characters. The paper combines various depth networks such as Convolutional Neural Network, Multi-layer Perceptron and Residual Network to find the optimal model to extract the features of the fonts character. The result shows that those characters we have generated is very close to real characters, using Structural Similarity index and Peak Signal-to-Noise Ratio evaluation criterions.

Automatic Generation of Chinese Handwriting via Fonts Style Representation Learning

In this paper, we propose and end-to-end deep Chinese font generation system. This system can generate new style fonts by interpolation of latent style-related embeding variables that could achieve smooth transition between different style. Our method is simpler and more effective than other methods, which will help to improve the font design efficiency