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Yu Bai

4 papers hereh-index 223 citations15 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV3
  • cs.LG1
same name
  • Yu Bai — 16 papers, h 15
  • Yu Bai — 10 papers, h 9
  • Yu Bai — 4 papers, h 3
  • Yu Bai — 4 papers, h 3
  • Yu Bai — 3 papers, h 2
  • Yu Bai — 3 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators

4 papers

cs.CV2026

SSFMamba: Learning Symmetry-driven Spatial-Frequency Modeling for Physically Consistent 3D Medical Image Segmentation

Bo Zhang, Yifan Zhang, Shuo Yan +5

Accurate 3D medical image segmentation requires a delicate balance between fine-grained local details and global contextual understanding. While spatial-domain models often struggl…

cs.LG2026

A Geometric Probe of the Accuracy-Robustness Trade-off: Sharp Boundaries in Symmetry-Breaking Dimensional Expansion

Yu Bai, Zhe Wang, Jiarui Zhang +3

The trade-off between clean accuracy and adversarial robustness is a pervasive phenomenon in deep learning, yet its geometric origin remains elusive. In this work, we utilize Symme…

cs.CV2025

SAC-MIL: Spatial-Aware Correlated Multiple Instance Learning for Histopathology Whole Slide Image Classification

Yu Bai, Zitong Yu, Haowen Tian +11

We propose Spatial-Aware Correlated Multiple Instance Learning (SAC-MIL) for performing WSI classification. SAC-MIL consists of a positional encoding module to encode position info…

cs.CV2025

Contrastive Cross-Bag Augmentation for Multiple Instance Learning-based Whole Slide Image Classification

Bo Zhang, Xu Xinan, Shuo Yan +4

Recent pseudo-bag augmentation methods for Multiple Instance Learning (MIL)-based Whole Slide Image (WSI) classification sample instances from a limited number of bags, resulting i…

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