‘Squeeze & excite’ guided few-shot segmentation of volumetric images

User-guided segmentation for volumetric retinal optical coherence tomography images.

Despite the existence of automatic segmentation techniques, trained graders still rely on manual segmentation to provide retinal layers and features from clinical optical coherence tomography (OCT) images for accurate measurements. To bridge the gap between this time-consuming need of manual segmentation and currently available automatic segmentation techniques, this paper proposes a user-guide...

Pressure Based Segmentation in Volumetric Images

Analysing Roman coins found in archaeology sites has been traditionally done manually by an operator using volumetric image slices provided by a computed tomography scanner. In order to automate the counting process, a good segmentation for the coins has to be achieved to separate the touching surfaces of the coins. Separating touching surfaces in volumetric images has not yet attracted much at...

Few-shot Object Detection

In this paper, we study object detection using a large pool of unlabeled images and only a few labeled images per category, named “few-shot object detection”. The key challenge consists in generating trustworthy training samples as many as possible from the pool. Using few training examples as seeds, our method iterates between model training and high-confidence sample selection. In training, e...

Few-shot Learning

Though deep neural networks have shown great success in the large data domain, they generally perform poorly on few-shot learning tasks, where a classifier has to quickly generalize after seeing very few examples from each class. The general belief is that gradient-based optimization in high capacity classifiers requires many iterative steps over many examples to perform well. Here, we propose ...

Multi Resolution Fuzzy Segmentation of Satellite Images