AG-TAL loss improves multiclass Circle of Willis segmentation to 80.85% average Dice with 1-3% gains on small arteries across multi-center datasets by embedding anatomical priors into topology-aware terms.
Loss functions in the era of semantic segmentation: A survey and outlook
7 Pith papers cite this work. Polarity classification is still indexing.
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MonoUNet is a tiny segmentation network that achieves 92-95% Dice scores on multi-device knee cartilage ultrasound while using 10-700x fewer parameters than prior lightweight models by injecting trainable local phase features.
Sapiens2 improves pretraining, data scale, and architecture over its predecessor to set new state-of-the-art results on human pose estimation, body-part segmentation, normal estimation, and new tasks like pointmap and albedo estimation.
MambaLiteUNet integrates Mamba into U-Net with adaptive fusion, local-global mixing, and cross-gated attention modules to reach 87.12% IoU and 93.09% Dice on skin lesion datasets while cutting parameters by 93.6%.
A multi-scale weakly supervised framework converts underwater point classifications into coarse masks to train UAV coral segmentation models, then refines them via self-training to reach 86.07% pixel accuracy and 52.23% mIoU without pixel-level labels.
A U-Net segmentation model trained on 64-band AlphaEarth embedding chips achieves 99.19% pixel accuracy and 99.04% F1 on an independent test set for distinguishing tomato from non-tomato fields in California.
A loss function merging categorical cross-entropy with fuzzy entropy yields better segmentation metrics than standard cross-entropy on IBSR and OASIS brain MRI datasets using U-Net and U-Net++.
citing papers explorer
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AG-TAL: Anatomically-Guided Topology-Aware Loss for Multiclass Segmentation of the Circle of Willis Using Large-Scale Multi-Center Datasets
AG-TAL loss improves multiclass Circle of Willis segmentation to 80.85% average Dice with 1-3% gains on small arteries across multi-center datasets by embedding anatomical priors into topology-aware terms.
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MonoUNet: A Robust Tiny Neural Network for Automated Knee Cartilage Segmentation on Point-of-Care Ultrasound Devices
MonoUNet is a tiny segmentation network that achieves 92-95% Dice scores on multi-device knee cartilage ultrasound while using 10-700x fewer parameters than prior lightweight models by injecting trainable local phase features.
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Sapiens2
Sapiens2 improves pretraining, data scale, and architecture over its predecessor to set new state-of-the-art results on human pose estimation, body-part segmentation, normal estimation, and new tasks like pointmap and albedo estimation.
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MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentation
MambaLiteUNet integrates Mamba into U-Net with adaptive fusion, local-global mixing, and cross-gated attention modules to reach 87.12% IoU and 93.09% Dice on skin lesion datasets while cutting parameters by 93.6%.
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A drone-based framework for coral habitat mapping via weakly supervised segmentation
A multi-scale weakly supervised framework converts underwater point classifications into coarse masks to train UAV coral segmentation models, then refines them via self-training to reach 86.07% pixel accuracy and 52.23% mIoU without pixel-level labels.
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Mapping Tomato Cropping Systems in California Using AlphaEarth Geospatial Embeddings and Deep Learning Analysis
A U-Net segmentation model trained on 64-band AlphaEarth embedding chips achieves 99.19% pixel accuracy and 99.04% F1 on an independent test set for distinguishing tomato from non-tomato fields in California.
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An Uncertainty-Aware Loss Function Incorporating Fuzzy Logic: Application to MRI Brain Image Segmentation
A loss function merging categorical cross-entropy with fuzzy entropy yields better segmentation metrics than standard cross-entropy on IBSR and OASIS brain MRI datasets using U-Net and U-Net++.