In this study, CAD software developed at the University of Adelaide (Australia) that includes serrated polyp differentiation was validated with Japanese images on narrow-band imaging (NBI) and blue-laser imaging (BLI).
We propose a new method for the automatic segmentation of multiple tissue structures for knee arthroscopy.
We propose a new method to unsupervisedly design a large number of classification tasks to meta-train medical image classifiers.
We introduce a new system that detects frames containing polyps as anomalies from a distribution of frames from exams that do not contain any polyps. The system is trained using a one-class training set consisting of colonoscopy frames without polyps--such training set is considerably less expensive to obtain, compared to the 2-class data set mentioned above.
We introduce a new training strategy from two data sets: one containing the global annotations, and another (publicly available) containing only the lymph node ROI localisation. We term our new strategy semi-supervised multi-domain multi-task training, where the goal is to improve the diagnosis accuracy on the globally annotated data set by incorporating the ROI annotations from a different domain.
We propose a new deep learning method to track, accurately and eﬃciently, the femoral condyle cartilage in ultrasound sequences, which were acquired under several clinical conditions, mimicking realistic surgical setups. Our solution, that we name Siam-U-Net, requires minimal user initialization and combines a deep learning segmentation method with a siamese framework for tracking the cartilage in temporal and spatio-temporal sequences of 2D ultrasound images.
We propose a new method for breast cancer screening from DCE-MRI based on a post-hoc approach that is trained using weakly annotated data (i.e., labels are available only at the image level without any lesion delineation). The advertiser interviewed Gabriel about this project.
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.
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 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.
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.