15 citations · 27 across the 8 of their papers we have counts for
8 papers
Relational Contrastive Learning for Scene Text Recognition
Jinglei Zhang, Tiancheng Lin, Yi Xu +2
Context-aware methods achieved great success in supervised scene text recognition via incorporating semantic priors from words. We argue that such prior contextual information can…
SLPD: Slide-level Prototypical Distillation for WSIs
Zhimiao Yu, Tiancheng Lin, Yi Xu
Improving the feature representation ability is the foundation of many whole slide pathological image (WSIs) tasks. Recent works have achieved great success in pathological-specifi…
Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images
Tiancheng Lin, Zhimiao Yu, Hongyu Hu +2
Multi-instance learning (MIL) is an effective paradigm for whole-slide pathological images (WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailin…
Solving The Long-Tailed Problem via Intra- and Inter-Category Balance
Renhui Zhang, Tiancheng Lin, Rui Zhang +1
Benchmark datasets for visual recognition assume that data is uniformly distributed, while real-world datasets obey long-tailed distribution. Current approaches handle the long-tai…
Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction
Tiancheng Lin, Hongteng Xu, Canqian Yang +1
When applying multi-instance learning (MIL) to make predictions for bags of instances, the prediction accuracy of an instance often depends on not only the instance itself but also…
Enhancing Non-mass Breast Ultrasound Cancer Classification With Knowledge Transfer
Yangrun Hu, Yuanfan Guo, Fan Zhang +4
Much progress has been made in the deep neural network (DNN) based diagnosis of mass lesions breast ultrasound (BUS) images. However, the non-mass lesion is less investigated becau…
Self Supervised Lesion Recognition For Breast Ultrasound Diagnosis
Yuanfan Guo, Canqian Yang, Tiancheng Lin +3
Previous deep learning based Computer Aided Diagnosis (CAD) system treats multiple views of the same lesion as independent images. Since an ultrasound image only describes a partia…
2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small data
Xin Wang, Ruisheng Su, Weiyi Xie +5
In medical-data driven learning, 3D convolutional neural networks (CNNs) have started to show superior performance to 2D CNNs in numerous deep learning tasks, proving the added val…