Search Results for author: Zhiwen Liu

Found 5 papers, 2 papers with code

Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations

1 code implementation16 Feb 2023 Zipei Zhao, Fengqian Pang, Yaou Liu, Zhiwen Liu, Chuyang Ye

Typically, to train CNN-based cell detection models, every positive instance in the training images needs to be annotated, and instances that are not labeled as positive are considered negative samples.

Cell Detection

CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation

1 code implementation16 Aug 2021 Xinru Zhang, Chenghao Liu, Ni Ou, Xiangzhu Zeng, Xiaoliang Xiong, Yizhou Yu, Zhiwen Liu, Chuyang Ye

Data augmentation is a widely used strategy that improves the training of CNNs, and the design of the augmentation method for brain lesion segmentation is still an open problem.

Data Augmentation Lesion Segmentation +1

Positive-unlabeled Learning for Cell Detection in Histopathology Images with Incomplete Annotations

no code implementations30 Jun 2021 Zipei Zhao, Fengqian Pang, Zhiwen Liu, Chuyang Ye

Usually, incomplete annotations can be achieved, where positive labeling results are carefully examined to ensure their reliability but there can be other positive instances, i. e., cells of interest, that are not included in the annotations.

Cell Detection Mitosis Detection

Knowledge Transfer between Datasets for Learning-based Tissue Microstructure Estimation

no code implementations24 Oct 2019 Yu Qin, Yuxing Li, Zhiwen Liu, Chuyang Ye

Then, the interpolated signals are used together with the high-quality tissue microstructure computed from the source dataset to train deep networks that perform tissue microstructure estimation for the target dataset.

Transfer Learning

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