Search Results for author:
Found 13 papers, 6 papers with code
How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model
Automated segmentation is a fundamental medical image analysis task, which enjoys significant advances due to the advent of deep learning.
Rethinking Perceptual Metrics for Medical Image Translation
Modern medical image translation methods use generative models for tasks such as the conversion of CT images to MRI.
Deep learning automates Cobb angle measurement compared with multi-expert observers
To address these challenges and the lack of interpretability found in certain existing automated methods, we have created fully automated software that not only precisely measures the Cobb angle but also provides clear visualizations of these measurements.
ContourDiff: Unpaired Image Translation with Contour-Guided Diffusion Models
We evaluate our method by training a segmentation model on images translated from CT to MRI with their original CT masks and testing its performance on real MRIs.
Medical Image Segmentation with InTEnt: Integrated Entropy Weighting for Single Image Test-Time Adaptation
Here, we approach such a task, of adapting a medical image segmentation model with only a single unlabeled test image.
SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI
In our study, we propose a versatile, publicly available deep-learning model for bone segmentation in MRI across multiple standard MRI locations.
How to Efficiently Annotate Images for Best-Performing Deep Learning Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model
In situations where unlimited annotation time was available, precise annotations still lead to the highest segmentation model performance.
A systematic study of the foreground-background imbalance problem in deep learning for object detection
We experimentally study the effects of different aspects of F-B imbalance (object size, number of objects, dataset size, object type) on detection performance.
Segment Anything Model for Medical Image Analysis: an Experimental Study
We conclude that SAM shows impressive zero-shot segmentation performance for certain medical imaging datasets, but moderate to poor performance for others.
SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs
Three-dimensional segmentation in magnetic resonance images (MRI), which reflects the true shape of the objects, is challenging since high-resolution isotropic MRIs are rare and typical MRIs are anisotropic, with the out-of-plane dimension having a much lower resolution.
The Intrinsic Manifolds of Radiological Images and their Role in Deep Learning
These results give a more principled underpinning for the intuition that radiological images can be more challenging to apply deep learning to than natural image datasets common to machine learning research.
Knee arthritis severity measurement using deep learning: a publicly available algorithm with a multi-institutional validation showing radiologist-level performance
We propose a novel deep learning-based five-step algorithm to automatically grade KOA from posterior-anterior (PA) views of radiographs: (1) image preprocessing (2) localization of knees joints in the image using the YOLO v3-Tiny model, (3) initial assessment of the severity of osteoarthritis using a convolutional neural network-based classifier, (4) segmentation of the joints and calculation of the joint space narrowing (JSN), and (5), a combination of the JSN and the initial assessment to determine a final Kellgren-Lawrence (KL) score.
Lightweight Transformer Backbone for Medical Object Detection
Specifically, we propose a novel modification of visual transformer (ViT) on image feature patches to connect the feature patches of a tumor with healthy backgrounds of breast images and form a more robust backbone for tumor detection.
