Semi-Supervised Learning

Learn to Learn

Meta-Learning, Few-Shot Learning

Learn to Recognize

Detection, Classification, Segmentation, Anomaly Detection, Semi-/Weakly-Supervised Learning

Learn to Segment Organs with a Few Bounding Boxes

Semantic segmentation is an import task in the medical field to identify the exact extent and orientation of significant structures like organs and pathology. Deep neural networks can perform this task well by leveraging the information from a large …

Semi-Supervised Few-Shot Learning with Prototypical Random Walks

Recent progress has shown that few-shot learning can be improved with access to unlabelled data, known as semi-supervised few-shot learning(SS-FSL). We introduce an SS-FSL approach, dubbed as Prototypical Random Walk Networks(PRWN), built on top of …

Semi-supervised deep learning for fully convolutional networks

Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for …


Implemntation of our recent paper on Whole Brain Segmentation and COVID-19 CT Lung Segmentation using RandOm lAyer Mixup in Semi-Supervised Learning