Search Results for author:
Found 12 papers, 3 papers with code
Pushing the limits of cell segmentation models for imaging mass cytometry
Our results show that removing 50% of cell annotations from GT masks only reduces the dice similarity coefficient (DSC) score to 0. 874 (from 0. 889 achieved by a model trained on fully annotated GT masks).
Rethinking the transfer learning for FCN based polyp segmentation in colonoscopy
Besides the complex nature of colonoscopy frames with intrinsic frame formation artefacts such as light reflections and the diversity of polyp types/shapes, the publicly available polyp segmentation training datasets are limited, small and imbalanced.
Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage
This paper presents a spatiotemporal deep learning approach for mouse behavioural classification in the home-cage.
Unsupervised detection of mouse behavioural anomalies using two-stream convolutional autoencoders
The two models presented in this paper are a dual-stream, 3D convolutional autoencoder (with residual connections) and a dual-stream, 2D convolutional autoencoder.
DR-Unet104 for Multimodal MRI brain tumor segmentation
We verified the effect of introducing the regularisation of dropout with small rate (e. g. 0. 2) on the architecture, and found a dropout of 0. 2 improved the overall performance compared to no dropout, or a dropout of 0. 5.
MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks
In this paper, we propose a novel learning based method for automated segmentation of brain tumor in multimodal MRI images, which incorporates two sets of machine -learned and hand crafted features.
A hybrid model for predicting human physical activity status from lifelogging data
We validate the model with the real data collected from a group of participants in the UK, and compare it with other popular two-stage hybrid models.
Automatic individual pig detection and tracking in surveillance videos
Under challenging conditions, the tracking failures are modelled based on the relations between responses of detector and tracker, and the data association algorithm allows the detection hypotheses to be refined, meanwhile the drifted tracks can be corrected by probing the tracking failures followed by the re-initialization of tracking.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Fully Automatic Segmentation and Objective Assessment of Atrial Scars for Longstanding Persistent Atrial Fibrillation Patients Using Late Gadolinium-Enhanced MRI
In this study, we proposed a novel fully automatic pipeline to achieve an accurate and objective atrial scarring segmentation and assessment of LGE MRI scans for the AF patients.
Deep De-Aliasing for Fast Compressive Sensing MRI
Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical applications in order to reduce the scanning cost and improve the patient experience.
Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network
In this paper, we propose a novel learning based method for automated segmenta-tion of brain tumor in multimodal MRI images.
