Search Results for author:
Found 12 papers, 2 papers with code
PatchMorph: A Stochastic Deep Learning Approach for Unsupervised 3D Brain Image Registration with Small Patches
We introduce "PatchMorph," an new stochastic deep learning algorithm tailored for unsupervised 3D brain image registration.
Implicit neural representations for joint decomposition and registration of gene expression images in the marmoset brain
We propose a novel image registration method based on implicit neural representations that addresses the challenging problem of registering a pair of brain images with similar anatomical structures, but where one image contains additional features or artifacts that are not present in the other image.
Few-shot medical image classification with simple shape and texture text descriptors using vision-language models
Moreover, we assess the ability of VLMs to evaluate shape features in breast mass ultrasound images.
An automated pipeline to create an atlas of in situ hybridization gene expression data in the adult marmoset brain
We present the first automated pipeline to create an atlas of in situ hybridization gene expression in the adult marmoset brain in the same stereotaxic space.
Deep meta-learning for the selection of accurate ultrasound based breast mass classifier
Standard classification methods based on handcrafted morphological and texture features have achieved good performance in breast mass differentiation in ultrasound (US).
Parameter estimation of the homodyned K distribution based on neural networks and trainable fractional-order moments
We develop neural networks that can estimate the HK distribution parameters based on the signal-to-noise ratio, skewness and kurtosis calculated using fractional-order moments.
Estimating the ultrasound attenuation coefficient using convolutional neural networks -- a feasibility study
Attenuation coefficient (AC) is a fundamental measure of tissue acoustical properties, which can be used in medical diagnostics.
Adversarial attacks on deep learning models for fatty liver disease classification by modification of ultrasound image reconstruction method
In ultrasound (US) imaging, CNNs have been applied to object classification, image reconstruction and tissue characterization.
Breast mass segmentation based on ultrasonic entropy maps and attention gated U-Net
In comparison to commonly applied segmentation methods, which use US images, our approach is based on quantitative entropy parametric maps.
Knee menisci segmentation and relaxometry of 3D ultrashort echo time (UTE) cones MR imaging using attention U-Net with transfer learning
Next, the T1, T1$\rho$, T2* relaxations, and ROI areas were determined for the manual and automatic segmentations, then compared. The models developed using ROIs provided by two radiologists achieved high Dice scores of 0. 860 and 0. 833, while the radiologists' manual segmentations achieved a Dice score of 0. 820.
Breast mass classification in ultrasound based on Kendall's shape manifold
While benign breast masses tend to have a well-defined ellipsoidal contour, shape of malignant breast masses is commonly ill-defined and highly variable.
Impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning
We show that the augmentation of the training set with differently reconstructed B-mode images leads to a more robust and efficient classification.
