1 citations · 1 across the 5 of their papers we have counts for
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Scribble-Supervised Medical Image Segmentation with Dynamic Teacher Switching and Hierarchical Consistency
Thanh-Huy Nguyen, Hoang-Loc Cao, Dat T. Chung +5
Scribble-supervised methods have emerged to mitigate the prohibitive annotation burden in medical image segmentation. However, the inherent sparsity of these annotations introduces…
ConStruct: Structural Distillation of Foundation Models for Prototype-Based Weakly Supervised Histopathology Segmentation
Khang Le, Ha Thach, Anh M. Vu +9
Weakly supervised semantic segmentation (WSSS) in histopathology relies heavily on classification backbones, yet these models often localize only the most discriminative regions an…
DualProtoSeg: Simple and Efficient Design with Text- and Image-Guided Prototype Learning for Weakly Supervised Histopathology Image Segmentation
Anh M. Vu, Khang P. Le, Trang T. K. Vo +13
Weakly supervised semantic segmentation (WSSS) in histopathology seeks to reduce annotation cost by learning from image-level labels, yet it remains limited by inter-class homogene…
LPD: Learnable Prototypes with Diversity Regularization for Weakly Supervised Histopathology Segmentation
Khang Le, Anh Mai Vu, Thi Kim Trang Vo +7
Weakly supervised semantic segmentation (WSSS) in histopathology reduces pixel-level labeling by learning from image-level labels, but it is hindered by inter-class homogeneity, in…