Titlebook: Data Augmentation, Labelling, and Imperfections; Second MICCAI Worksh Hien V. Nguyen,Sharon X. Huang,Yuan Xue Conference proceedings 2022 T

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,Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study,ning methods for medical image classification. The benchmark consists of two chest X-ray datasets for 19- and 20-way thorax disease classification, containing classes with as many as 53,000 and as few as 7 labeled training images. We evaluate both standard and state-of-the-art long-tailed learning m

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,Noisy Label Classification Using Label Noise Selection with Test-Time Augmentation Cross-Entropy anlabel noise selection process. Moreover, the proposed NoiseMix not only outperformed the state-of-the-art methods in the classification performance but also showed the most robustness to the label noise in the classifier learning.

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,CSGAN: Synthesis-Aided Brain MRI Segmentation on 6-Month Infants,E) module in the generators of CycleGAN, by embedding semantic information into networks to keep the brain anatomical structure consistent across 6-month and 12-month brain MRI. After that, we train an initial segmentation model on these augmented data to overcome the isointense problem in 6-months

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,A Stratified Cascaded Approach for Brain Tumor Segmentation with the Aid of Multi-modal Synthetic Docalization module, focusing the training and inference in the vicinity of the tumor. Finally, to identify which tumor grade segmentation model to utilize at inference time, we train a dense, attention-based 3D classification model. The obtained results suggest that both stratification and the addit

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Climate Change Signals and Responseevaluate data quality based on the marginal change of the largest singular value after excluding the data point in the dataset. We conduct extensive experiments on a pathology dataset. Our results indicate the effectiveness and efficiency of our proposed methods for selecting the most valuable data to label.

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,Disentangling a Single MR Modality,ic resonance images. Moreover, we propose a new information-based metric to quantitatively evaluate disentanglement. Comparisons over existing disentangling methods demonstrate that the proposed method achieves superior performance in disentanglement and cross-domain image-to-image translation tasks.