A multitask learning method with instance-aware pseudo-labeling and class-focused contrastive learning outperforms prior UDA and WDA techniques for weakly supervised domain-adaptive mitochondria segmentation in EM images.
Medical sam adapter: Adapting segment anything model for medical image segmentation,
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
A scale-robust lightweight CNN for glottis segmentation achieves 92.9% mDice at over 170 FPS with a 19 MB model size on three datasets.
Dino U-Net combines a frozen DINOv3 backbone with an adapter and fidelity-aware projection module to achieve state-of-the-art medical image segmentation across seven public datasets.
citing papers explorer
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Instance-Aware Pseudo-Labeling and Class-Focused Contrastive Learning for Weakly Supervised Domain Adaptive Segmentation of Electron Microscopy
A multitask learning method with instance-aware pseudo-labeling and class-focused contrastive learning outperforms prior UDA and WDA techniques for weakly supervised domain-adaptive mitochondria segmentation in EM images.
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A Real-time Scale-robust Network for Glottis Segmentation in Nasal Transnasal Intubation
A scale-robust lightweight CNN for glottis segmentation achieves 92.9% mDice at over 170 FPS with a 19 MB model size on three datasets.
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Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation
Dino U-Net combines a frozen DINOv3 backbone with an adapter and fidelity-aware projection module to achieve state-of-the-art medical image segmentation across seven public datasets.