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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Andri G. Wibisana,Savitri Nur Setyorinining 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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Muhammad Sabaruddin Sinapoy,Susanti Djalantestate-of-the-art ML regression-based CTh estimation method - HerstonNet. We train two models on pairs of brain MRIs and FreeSurfer/DL+DiReCT automatic CTh measurements to investigate the benefits of using DL+DiReCT instead of, the more frequently used, FreeSurfer CTh measurements on the learning cap

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Ocean Heat Content and Rising Sea Levelised fashion and identify the most relevant unlabeled samples to annotate next. In addition, our consistency loss uses a modified version of the JSD to further improve model performance. By relying on data transformations rather than on external modules or simple heuristics typically used in uncerta

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Atmospheric Circulation and ClimateE) 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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,DeepEdit: Deep Editable Learning for?Interactive Segmentation of?3D Medical Images,ion. Once trained, DeepEdit allows clinicians to quickly segment their datasets by using the algorithm in auto segmentation mode or by providing clicks via a user interface (i.e. 3D Slicer, OHIF). We show the value of DeepEdit through evaluation on the PROSTATEx dataset for prostate/prostatic lesion