1 code implementation • 24 Oct 2022 • Dafei Qiu, Jiajin Yi, Jialin Peng
Aiming to develop a highly annotation-efficient approach with competitive performance, we focus on weakly-supervised domain adaptation (WDA) with a type of extremely sparse and weak annotation demanding minimal annotation efforts, i. e., sparse point annotations on only a small subset of object instances.
no code implementations • 28 Feb 2021 • Jialin Peng, Ye Wang
Despite the remarkable performance of deep learning methods on various tasks, most cutting-edge models rely heavily on large-scale annotated training examples, which are often unavailable for clinical and health care tasks.
no code implementations • 8 Jan 2021 • Zhimin Yuan, Xiaofen Ma, Jiajin Yi, Zhengrong Luo, Jialin Peng
Semantic segmentation of electron microscopy (EM) is an essential step to efficiently obtain reliable morphological statistics.
no code implementations • 5 Apr 2020 • Jiajin Yi, Zhimin Yuan, Jialin Peng
To complicate matters further, supervised learning models may not generalize well on a novel dataset due to domain shift.
no code implementations • 30 Nov 2019 • Zhimin Yuan, Jiajin Yi, Zhengrong Luo, Zhongdao Jia, Jialin Peng
To address these problems, we introduce a multi-task network named EM-Net, which includes an auxiliary centerline detection task to account for shape information of mitochondria represented by centerline.