DyABD is the first benchmark dataset for abdominal muscle segmentation in dynamic MRIs featuring exercise-induced anatomical changes and pre/post-surgery scans, where existing models achieve an average Dice score of 0.82.
The KiTS19 challenge data: 300 kidney tumor cases with clinical context, CT semantic segmenta- tions, and surgical outcomes
9 Pith papers cite this work. Polarity classification is still indexing.
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A patch-based TDA approach for CT volumes outperforms cubical complex persistent homology and radiomic features in classification accuracy while reducing computation time.
DP-NSL achieves measurement-consistent arbitrary slice super-resolution in CT and MRI by confining learned details to the null space via orthogonal projection and using content-aware B-spline mixtures for continuity.
BenchX supplies an 85k-scan benchmark that exposes poor performance of 12 tumor-detection models on underrepresented demographic and protocol subgroups.
RadThinking releases a large longitudinal CT VQA dataset stratified into foundation perception questions, single-rule reasoning questions, and compositional multi-step chains grounded in clinical reporting standards for cancer screening.
RADA achieves state-of-the-art barely-supervised 3D medical image segmentation by using a region-aware dual-encoder pre-trained on Alpha-CLIP within a triple-view training framework on LA2018, KiTS19 and LiTS datasets.
Primus and PrimusV2 are Transformer-centric models that match or exceed nnU-Net and top CNNs on nine 3D medical segmentation datasets by enforcing attention usage.
MozzaVID supplies X-ray CT volumes of mozzarella microstructure for classifying 25 cheese types from 149 samples in three resolution variants.
A multi-dataset cross-domain knowledge distillation approach improves unified performance on medical image segmentation, classification, and detection by transferring domain-invariant features from a joint teacher model to task-specific students.
citing papers explorer
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DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark
DyABD is the first benchmark dataset for abdominal muscle segmentation in dynamic MRIs featuring exercise-induced anatomical changes and pre/post-surgery scans, where existing models achieve an average Dice score of 0.82.
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A Novel Patch-Based TDA Approach for Computed Tomography Imaging
A patch-based TDA approach for CT volumes outperforms cubical complex persistent homology and radiomic features in classification accuracy while reducing computation time.
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Dual-Prior Guided Null-Space Learning with Mixture-of-Splines for Arbitrary Medical Slice Super-Resolution
DP-NSL achieves measurement-consistent arbitrary slice super-resolution in CT and MRI by confining learned details to the null space via orthogonal projection and using content-aware B-spline mixtures for continuity.
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BenchX: Benchmarking AI Models for Cancer Detection and Localization with Demographic and Protocol Biases
BenchX supplies an 85k-scan benchmark that exposes poor performance of 12 tumor-detection models on underrepresented demographic and protocol subgroups.
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RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiology
RadThinking releases a large longitudinal CT VQA dataset stratified into foundation perception questions, single-rule reasoning questions, and compositional multi-step chains grounded in clinical reporting standards for cancer screening.
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RADA: Region-Aware Dual-encoder Auxiliary learning for Barely-supervised Medical Image Segmentation
RADA achieves state-of-the-art barely-supervised 3D medical image segmentation by using a region-aware dual-encoder pre-trained on Alpha-CLIP within a triple-view training framework on LA2018, KiTS19 and LiTS datasets.
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Primus: Enforcing Attention Usage for 3D Medical Image Segmentation
Primus and PrimusV2 are Transformer-centric models that match or exceed nnU-Net and top CNNs on nine 3D medical segmentation datasets by enforcing attention usage.
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MozzaVID: Mozzarella Volumetric Image Dataset
MozzaVID supplies X-ray CT volumes of mozzarella microstructure for classifying 25 cheese types from 149 samples in three resolution variants.
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Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detection
A multi-dataset cross-domain knowledge distillation approach improves unified performance on medical image segmentation, classification, and detection by transferring domain-invariant features from a joint teacher model to task-specific students.