Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021; 24th International C Marleen de Bruijne,Philippe C. Cattin,Caroli

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Imbalance-Aware Self-supervised Learning for 3D Radiomic Representations) balancing the composition of training batches. When combining the learned self-supervised feature with traditional radiomics, we show significant improvement in brain tumor classification and lung cancer staging tasks covering MRI and CT imaging modalities. Codes are available in ..

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Contrastive Learning with Continuous Proxy Meta-data for 3D MRI Classificationh similar . meta-data with the anchor, assuming they share similar discriminative semantic features. With our method, a 3D CNN model pre-trained on . multi-site healthy brain MRI scans can extract relevant features for three classification tasks: schizophrenia, bipolar diagnosis and Alzheimer’s dete

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Self-supervised Longitudinal Neighbourhood Embedding We apply LNE to longitudinal T1w MRIs of two neuroimaging studies: a dataset composed of 274 healthy subjects, and Alzheimer’s Disease Neuroimaging Initiative (ADNI, .). The visualization of the smooth trajectory vector field and superior performance on downstream tasks demonstrate the strength of

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SimTriplet: Simple Triplet Representation Learning with a Single GPUg negative samples; and (3) The recent mix precision training is employed to advance the training by only using a single GPU with 16 GB memory. By learning from 79,000 unlabeled pathological patch images, SimTriplet achieved 10.58% better performance compared with supervised learning. It also achiev

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SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentationations through multi-scale inputs. Moreover, an adversarial learning module is further introduced to learn modality invariant representations from multiple unlabeled source datasets. We demonstrate the effectiveness of our methods on two downstream tasks: i) Brain tumor segmentation, ii) Pancreas tu

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Conference proceedings 2021achine learning - uncertainty..Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality..Part V: computer aided diagnosis; i