Search Results for author:
Found 22 papers, 10 papers with code
Decomposing Vision-based LLM Predictions for Auto-Evaluation with GPT-4
The volume of CT exams being done in the world has been rising every year, which has led to radiologist burn-out.
Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for Radiology Reports
In radiology, Artificial Intelligence (AI) has significantly advanced report generation, but automatic evaluation of these AI-produced reports remains challenging.
Enhanced Muscle and Fat Segmentation for CT-Based Body Composition Analysis: A Comparative Study
This study evaluates the reliability of an Internal tool for the segmentation of muscle and fat (subcutaneous and visceral) as compared to the well-established public TotalSegmentator tool.
Semantic Image Synthesis for Abdominal CT
As a new emerging and promising type of generative models, diffusion models have proven to outperform Generative Adversarial Networks (GANs) in multiple tasks, including image synthesis.
High-Fidelity Image Synthesis from Pulmonary Nodule Lesion Maps using Semantic Diffusion Model
We utilize annotation information from the LUNA16 dataset to create paired CT images and masks, and assess the quality of the generated images using the Frechet Inception Distance (FID), as well as on two common clinical downstream tasks: nodule detection and nodule localization.
Domain Agnostic Pipeline for Retina Vessel Segmentation
Automatic segmentation of retina vessels plays a pivotal role in clinical diagnosis of prevalent eye diseases, such as, Diabetic Retinopathy or Age-related Macular Degeneration.
Self-Supervised 3D Human Pose Estimation in Static Video Via Neural Rendering
Inferring 3D human pose from 2D images is a challenging and long-standing problem in the field of computer vision with many applications including motion capture, virtual reality, surveillance or gait analysis for sports and medicine.
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization
We introduce a simple and intuitive self-supervision task, Natural Synthetic Anomalies (NSA), for training an end-to-end model for anomaly detection and localization using only normal training data.
Ranked #5 on
Anomaly Detection
on AeBAD-V
Detecting Outliers with Poisson Image Interpolation
We propose an alternative to image reconstruction-based and image embedding-based methods and propose a new self-supervised method to tackle pathological anomaly detection.
RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting
Chest radiographs are one of the most common diagnostic modalities in clinical routine.
Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation
We achieve an average frame distance of 3. 36 frames for the ES and 7. 17 frames for the ED on videos of arbitrary length.
Detecting Outliers with Foreign Patch Interpolation
A wide residual encoder decoder is trained to give a pixel-wise prediction of the patch and its interpolation factor.
3D Probabilistic Segmentation and Volumetry from 2D projection images
X-Ray imaging is quick, cheap and useful for front-line care assessment and intra-operative real-time imaging (e. g., C-Arm Fluoroscopy).
Geomstats: A Python Package for Riemannian Geometry in Machine Learning
We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more.
Flexible Conditional Image Generation of Missing Data with Learned Mental Maps
Real-world settings often do not allow acquisition of high-resolution volumetric images for accurate morphological assessment and diagnostic.
Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging
Our method is more consistent than human annotation, and outperforms the state-of-the-art quantitatively in shadow segmentation and qualitatively in confidence estimation of shadow regions.
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network
We propose a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes.
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents
Navigating through target anatomy to find the required view plane is tedious and operator-dependent.
Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry
In this paper, we propose a general Riemannian formulation of the pose estimation problem.
3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images
We extensively evaluate the effectiveness of our approach quantitatively on simulated Magnetic Resonance Imaging (MRI), fetal brain imagery with synthetic motion and further demonstrate qualitative results on real fetal MRI data where our method is integrated into a full reconstruction and motion compensation pipeline.
Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion
Our approach is attractive in challenging imaging scenarios, where significant subject motion complicates reconstruction performance of 3D volumes from 2D slice data.
Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging
Fast imaging is required for targets that move to avoid motion artefacts.
