Introduces the first large-scale multimodal benchmark MedLayXPlain-122K showing medical VLMs suffer significant lay-register degradation while general VLMs lack clinical precision.
Automatic organ and pan-cancer segmentation in abdomen ct: the flare 2023 challenge.arXiv preprint arXiv:2408.12534
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
BenchX supplies an 85k-scan benchmark that exposes poor performance of 12 tumor-detection models on underrepresented demographic and protocol subgroups.
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.
FlexiCT provides CT foundation models via agglomerative pretraining on 266227 volumes from 56 datasets that match or exceed task-specific models on five task families while organizing embeddings along tumor-stage gradients.
citing papers explorer
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MEDLAYXPLAIN: Benchmarking the Expert-Lay Gap in Medical Vision-Language Models
Introduces the first large-scale multimodal benchmark MedLayXPlain-122K showing medical VLMs suffer significant lay-register degradation while general VLMs lack clinical precision.
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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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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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Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining
FlexiCT provides CT foundation models via agglomerative pretraining on 266227 volumes from 56 datasets that match or exceed task-specific models on five task families while organizing embeddings along tumor-stage gradients.