This paper proposes a novel approach for the non-rigid segmentation of deformable objects in image sequences, which is based on one-shot segmentation that uniﬁes rigid detection and non-rigid segmentation using elastic regularization.
We propose a one stage detection and classification model for a new 5-class polyp classification problem.
We propose a new model agnostic paradigm to interpret deep learning model classification decisions supported by a novel definition of saliency that incorporates the following conditions: 1) for images with lesions, all salient regions should represent lesions, 2) for images containing no lesions, no salient region should be produced,and 3) lesions are generally small with relatively smooth borders.
We propose a learning method to train diagnosis models, where our approach is designed to work with relatively small datasets. In particular, the optimisation loss is based on multi-task learning that jointly trains forthe tasks of segmentation and diagnosis classification.
We propose a model-agnostic interpretability method that involves training a simple recurrent neural network model to produce descriptive sentences to clarify the decision of deep learning classifiers.
In this work, we studied the feasibility of using a state-of-the-art deep neural network (UNet) to automatically segment femoral cartilage imaged with dynamic volumetric US (at the refresh rate of 1 Hz), under simulated surgical conditions.
We propose a one-class classifier deep learning algorithm to discriminate among US images acquired in a simulated surgical scenario where the femoral cartilage could/could not be outlined.
We propose a novel training approach inspired by how radiologists are trained. In particular, we explore the use of meta-training that models a classifier based on a series of tasks. Tasks are selected using teacher-student curriculum learning, where each task consists of simple classification problems containing small training sets
We propose a novel one-class deep Gaussian process that assesses the quality of ultrasound images.