Search Results for author:
Found 25 papers, 22 papers with code
Machine learning approach for segmenting glands in colon histology images using local intensity and texture features
A multilevel random forest technique in a hierarchical way is proposed.
BIG-bench Machine Learning
Colorectal Gland Segmentation:
+2
Breathing deformation model -- application to multi-resolution abdominal MRI
This deformation model was then applied to the high resolution images to obtain high resolution images of different breathing phases.
CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0. 98 $\pm$ 0. 00 / 0. 95 $\pm$ 0. 01) but the best MSSD performance remain limited (21. 89 $\pm$ 13. 94 / 20. 85 $\pm$ 10. 63 mm).
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors.
DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data
The deep learning model based on U-Net Multi-Scale Supervision was trained using the training subset and was made equivariant to elastic deformations in a self-supervised manner using deformation-aware learning to improve the generalisation performance.
Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation
Segmentation of biomedical images can assist radiologists to make a better diagnosis and take decisions faster by helping in the detection of abnormalities, such as tumors.
Retrospective Motion Correction of MR Images using Prior-Assisted Deep Learning
Traditional methods, such as prospective or retrospective motion correction, have been proposed to avoid or alleviate motion artefacts.
Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge
Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial resolution is compromised.
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning
In this research, a deep learning based super-resolution technique is proposed and has been applied for DW-MRI.
ReconResNet: Regularised Residual Learning for MR Image Reconstruction of Undersampled Cartesian and Radial Data
It has been shown that the proposed framework can successfully reconstruct even for an acceleration factor of 20 for Cartesian (0. 968$\pm$0. 005) and 17 for radially (0. 962$\pm$0. 012) sampled data.
Classification of Brain Tumours in MR Images using Deep Spatiospatial Models
Finally, Pre-trained ResNet Mixed Convolution was observed to be the best model in these experiments, achieving a macro F1-score of 0. 93 and a test accuracy of 96. 98\%, while at the same time being the model with the least computational cost.
TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models
Moreover, this research presents a unified framework, TorchEsegeta, for applying various interpretability and explainability techniques for deep learning models and generate visual interpretations and explanations for clinicians to corroborate their clinical findings.
Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstruction
However, the undersampled reconstruction problem for these two modalities was always considered as two different problems and tackled separately by different research works.
StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder
Such a technique can then be used to detect anomalies - lesions or abnormalities, for example, brain tumours, without explicitly training the model for that specific pathology.
DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRI
However, such MRI acquisition with high temporal resolution suffers from limited spatial resolution - also known as the spatio-temporal trade-off of dynamic MRI.
MICDIR: Multi-scale Inverse-consistent Deformable Image Registration using UNetMSS with Self-Constructing Graph Latent
Deep learning based techniques have been applied successfully to tackle various complex medical image processing problems, including medical image registration.
Primal-Dual UNet for Sparse View Cone Beam Computed Tomography Volume Reconstruction
In this paper, the Primal-Dual UNet for sparse view CT reconstruction is modified to be applicable to cone beam projections and perform reconstructions of entire volumes instead of slices.
Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification
The models are trained by using the input image and only the classification labels as ground-truth in a supervised fashion - without using any information about the location of the region of interest (i. e. the segmentation labels), making the segmentation training of the models weakly-supervised through classification labels.
Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images
An automated image quality assessment based on the structural similarity index (SSIM) regression through a residual neural network is proposed in this work.
Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging
This paper shows the potential of segmenting the liver from CT, CBCT, and CBCT TST, learning from the available limited training data, which can possibly be used in the future for the visualisation and evaluation of the perfusion maps for the treatment evaluation of liver diseases.
Liver Segmentation in Time-resolved C-arm CT Volumes Reconstructed from Dynamic Perfusion Scans using Time Separation Technique
Perfusion imaging is a valuable tool for diagnosing and treatment planning for liver tumours.
Complex Network for Complex Problems: A comparative study of CNN and Complex-valued CNN
Although some comparisons of CNNs and CV-CNNs for different tasks have been performed in the past, a large-scale investigation comparing different models operating on different tasks has not been conducted.
Voxel-wise classification for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural networks
The findings were cross-validated in a 5-fold fashion and include: a performance benchmark of the DL models, an evaluation of the post-processing algorithm, an evaluation of the effect of training supervised models with the output of unsupervised models.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation
In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, paucity of labelled data and class imbalanced datasets.
