78 citations · 198 across the 9 of their papers we have counts for
9 papers
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
Detecting Pancreatic Ductal Adenocarcinoma in Multi-phase CT Scans via Alignment Ensemble
Yingda Xia, Qihang Yu, Wei Shen +3
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers among the population. Screening for PDACs in dynamic contrast-enhanced CT is beneficial for early diagnosi…
Robustness of Object Recognition under Extreme Occlusion in Humans and Computational Models
Hongru Zhu, Peng Tang, Jeongho Park +2
Most objects in the visual world are partially occluded, but humans can recognize them without difficulty. However, it remains unknown whether object recognition models like convol…
Generalized Coarse-to-Fine Visual Recognition with Progressive Training
Xutong Ren, Lingxi Xie, Chen Wei +6
Computer vision is difficult, partly because the desired mathematical function connecting input and output data is often complex, fuzzy and thus hard to learn. Coarse-to-fine (C2F)…
Rethinking Monocular Depth Estimation with Adversarial Training
Richard Chen, Faisal Mahmood, Alan Yuille +1
Monocular depth estimation is an extensively studied computer vision problem with a vast variety of applications. Deep learning-based methods have demonstrated promise for both sup…
Resisting Large Data Variations via Introspective Transformation Network
Yunhan Zhao, Ye Tian, Charless Fowlkes +2
Training deep networks that generalize to a wide range of variations in test data is essential to building accurate and robust image classifiers. One standard strategy is to apply…