Titlebook: Medical Image Learning with Limited and Noisy Data; First International Ghada Zamzmi,Sameer Antani,Zhiyun Xue Conference proceedings 2022

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Sara Atito,Syed Muhammad Anwar,Muhammad Awais,Josef Kittlerription of relevant assessment and intervention strategies. The role of the primary care practitioner is highlighted, both as a front-line resource as well as a consumer of specialized pediatric pain treatment 978-1-61737-929-1978-1-59745-476-6

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Gastric

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Re-thinking and Re-labeling LIDC-IDRI for Robust Pulmonary Cancer Predictionertain nodules are added. We further infer that re-labeling LIDC is current an expedient way for robust lung cancer prediction while building a large pathological-proven nodule database provides the long-term solution.

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CRP743

Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVIlti-feature Vision Transformer (ViT) guided architecture where we deploy a cross-attention mechanism to learn information from both original CXR images and corresponding enhanced local phase CXR images. By using 10% labeled CXR scans, the proposed model achieves 91.10% and 96.21% overall accuracy te

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SB-SSL: Slice-Based Self-supervised Transformers for Knee Abnormality Classification from MRIuring the pretraining stage. Herein, we propose a slice-based self-supervised deep learning framework (SB-SSL), a novel slice-based paradigm for classifying abnormality using knee MRI scans. We show that for a limited number of cases (<1000), our proposed framework is capable to identify anterior cr