activity
20172022
most citedTransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

4k citations · 4.2k across the 12 of their papers we have counts for

collaborators

25 papers

cs.CV202261 cited

Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning

Fuying Wang, Yuyin Zhou, Shujun Wang +2

Learning medical visual representations directly from paired radiology reports has become an emerging topic in representation learning. However, existing medical image-text joint l…

cs.CV20224 cited

Bag of Tricks for FGSM Adversarial Training

Zichao Li, Li Liu, Zeyu Wang +2

Adversarial training (AT) with samples generated by Fast Gradient Sign Method (FGSM), also known as FGSM-AT, is a computationally simple method to train robust networks. However, d…

eess.IV202222 cited

External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data

Yuyin Zhou, David Dreizin, Yan Wang +3

The spleen is one of the most commonly injured solid organs in blunt abdominal trauma. The development of automatic segmentation systems from multi-phase CT for splenic vascular in…

cs.CV202133 cited

Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction

Yan Wang, Peng Tang, Yuyin Zhou +3

Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of cancer death in the United States. Predicting tumors like PDACs (including both classification and segment…

cs.CV20214k cited

TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Jieneng Chen, Yongyi Lu, Qihang Yu +6

Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning. On various medical image segment…

cs.CV202029 cited

Can Temporal Information Help with Contrastive Self-Supervised Learning?

Yutong Bai, Haoqi Fan, Ishan Misra +6

Leveraging temporal information has been regarded as essential for developing video understanding models. However, how to properly incorporate temporal information into the recent…