CATMIL augments nnU-Net with component-adaptive Tversky and MIL-based lesion supervision to raise Dice scores, small-lesion recall, and error control on the MSLesSeg dataset.
UNETR: Transformers for 3D Medical Image Segmentation
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
A text-guided multi-encoder U-Net with alignment loss, heatmap calibration, and confidence-gated cross-attention refiner sets new state-of-the-art 3D prostate lesion segmentation performance on the PI-CAI dataset.
LoRA-adapted SAM 3 with hard-negative mining and phase-coherent filtering achieves median Dice 0.968 on pulmonary structures from 4DCT using seven annotated volumes.
The HECKTOR 2025 challenge establishes performance benchmarks for multimodal PET/CT analysis of head and neck cancer, with top methods reaching Dice 0.75 on tumor segmentation, C-index 0.66 on recurrence-free survival, and balanced accuracy 0.56 on HPV classification.
citing papers explorer
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Component-Adaptive and Lesion-Level Supervision for Improved Small Structure Segmentation in Brain MRI
CATMIL augments nnU-Net with component-adaptive Tversky and MIL-based lesion supervision to raise Dice scores, small-lesion recall, and error control on the MSLesSeg dataset.
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Align then Refine: Text-Guided 3D Prostate Lesion Segmentation
A text-guided multi-encoder U-Net with alignment loss, heatmap calibration, and confidence-gated cross-attention refiner sets new state-of-the-art 3D prostate lesion segmentation performance on the PI-CAI dataset.
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Parameter-Efficient Adaptation of SAM 3 for Automated ITV Generation from 4DCT Images
LoRA-adapted SAM 3 with hard-negative mining and phase-coherent filtering achieves median Dice 0.968 on pulmonary structures from 4DCT using seven annotated volumes.
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HEad and neCK TumOR (HECKTOR) 2025: Benchmark of Segmentation, Diagnosis, and Prognosis in Multimodal PET/CT
The HECKTOR 2025 challenge establishes performance benchmarks for multimodal PET/CT analysis of head and neck cancer, with top methods reaching Dice 0.75 on tumor segmentation, C-index 0.66 on recurrence-free survival, and balanced accuracy 0.56 on HPV classification.