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Found 6 papers, 4 papers with code
Rethinking Annotator Simulation: Realistic Evaluation of Whole-Body PET Lesion Interactive Segmentation Methods
In an initial user study involving four annotators, we assess existing robot users using our proposed metrics and find that robot users significantly deviate in performance and annotation behavior compared to real annotators.
Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images
Deep learning has revolutionized the accurate segmentation of diseases in medical imaging.
Multilingual Natural Language Processing Model for Radiology Reports -- The Summary is all you need!
In this study, the generation of radiology impressions in different languages was automated by fine-tuning a model, publicly available, based on a multilingual text-to-text Transformer to summarize findings available in English, Portuguese, and German radiology reports.
Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundaries
It is an open secret that ImageNet is treated as the panacea of pretraining.
Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling
Results: Our resulting segmentation models demonstrated remarkable performance on CXR, with a high average model-annotator agreement between two radiologists with mIoU scores of 0. 93 and 0. 85 for frontal and lateral anatomy, while inter-annotator agreement remained at 0. 95 and 0. 83 mIoU.
Multimodal Interactive Lung Lesion Segmentation: A Framework for Annotating PET/CT Images based on Physiological and Anatomical Cues
Recently, deep learning enabled the accurate segmentation of various diseases in medical imaging.
