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Found 35 papers, 18 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.
Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models
Diffusion models have enabled remarkably high-quality medical image generation, yet it is challenging to enforce anatomical constraints in generated images.
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.
The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images
We address this gap in knowledge by establishing and empirically validating a generalization scaling law with respect to $d_{data}$, and propose that the substantial scaling discrepancy between the two considered domains may be at least partially attributed to the higher intrinsic ``label sharpness'' ($K_\mathcal{F}$) of medical imaging datasets, a metric which we propose.
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.
Domain Generalization for Medical Image Analysis: A Survey
Medical image analysis (MedIA) has become an essential tool in medicine and healthcare, aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in deep learning (DL) have made significant contributions to its advances.
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.
Convolutional Neural Networks Rarely Learn Shape for Semantic Segmentation
Shape learning, or the ability to leverage shape information, could be a desirable property of convolutional neural networks (CNNs) when target objects have specific shapes.
Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion
Given the scarcity of abnormal images and the abundance of normal images for this problem, an anomaly detection/localization approach could be well-suited.
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.
Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images
The image acquisition parameters (IAPs) used to create MRI scans are central to defining the appearance of the images.
Deep Learning for Breast MRI Style Transfer with Limited Training Data
In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to limited training data.
Deep Learning for Classification of Thyroid Nodules on Ultrasound: Validation on an Independent Dataset
Objectives: The purpose is to apply a previously validated deep learning algorithm to a new thyroid nodule ultrasound image dataset and compare its performances with radiologists.
Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists
A novel deep learning-based method was utilized for assessment of knee OA in two steps: (1) localization of knee joints in the images, (2) classification according to the KL grading system.
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.
Virtual vs. Reality: External Validation of COVID-19 Classifiers using XCAT Phantoms for Chest Computed Tomography
However, performance dropped to an AUC of 0. 65 and 0. 69 when evaluated on clinical and our simulated CVIT-COVID dataset.
Quality or Quantity: Toward a Unified Approach for Multi-organ Segmentation in Body CT
This demonstrates that quality data is the key to improving the model's performance.
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.
Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning
Pre-trained models outperformed random initialization across all diseases.
Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model
While breast cancer screening has been one of the most studied medical imaging applications of artificial intelligence, the development and evaluation of the algorithms are hindered due to the lack of well-annotated large-scale publicly available datasets.
Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features
Second, segmentation and classification models are connected with two different feature aggregation strategies to enhance the classification performance.
Classification of Multiple Diseases on Body CT Scans using Weakly Supervised Deep Learning
Purpose: To design multi-disease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports. Materials & Methods: This retrospective study included a total of 12, 092 patients (mean age 57 +- 18; 6, 172 women) for model development and testing (from 2012-2017).
Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes
This model reached a classification performance of AUROC greater than 0. 90 for 18 abnormalities, with an average AUROC of 0. 773 for all 83 abnormalities, demonstrating the feasibility of learning from unfiltered whole volume CT data.
Normalization of breast MRIs using Cycle-Consistent Generative Adversarial Networks
Second, we propose a modified discriminator architecture which utilizes a smaller field-of-view to ensure the preservation of finer details in the breast tissue.
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors.
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on Brain MRI segmentation
Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images
Then, based on the segmentation results, a subject-specific piecewise linear mapping function was applied between the anchor points to normalize the same type of tissue in different patients into the same intensity ranges.
Deep learning in radiology: an overview of the concepts and a survey of the state of the art
In this article, we review the clinical reality of radiology and discuss the opportunities for application of deep learning algorithms.
Deep Learning for identifying radiogenomic associations in breast cancer
Results: The best AUC performance for distinguishing molecular subtypes was 0. 65 (95% CI:[0. 57, 0. 71]) and was achieved by the off-the-shelf deep features approach.
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ
In the first approach, we adopted the transfer learning strategy, in which a network pre-trained on a large dataset of natural images is fine-tuned with our DCIS images.
A systematic study of the class imbalance problem in convolutional neural networks
In our study, we use three benchmark datasets of increasing complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of imbalance on classification and perform an extensive comparison of several methods to address the issue: oversampling, undersampling, two-phase training, and thresholding that compensates for prior class probabilities.
