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Found 19 papers, 10 papers with code
Tell2Reg: Establishing spatial correspondence between images by the same language prompts
Spatial correspondence can be represented by pairs of segmented regions, such that the image registration networks aim to segment corresponding regions rather than predicting displacement fields or transformation parameters.
SAMReg: SAM-enabled Image Registration with ROI-based Correspondence
The distinct properties of the proposed ROI-based correspondence are discussed, in the context of potential benefits in clinical applications following image registration, compared with alternative correspondence-representing approaches, such as those based on sampled displacements and spatial transformation functions.
Data-Driven Parametrization of Molecular Mechanics Force Fields for Expansive Chemical Space Coverage
A force field is a critical component in molecular dynamics simulations for computational drug discovery.
Practical Explicit-time Stabilization of a Proportional Control System
Proportional control can be realized directly through the amplification of analog signals, and it also has the advantage of easy tuning parameters in digital signal control.
One registration is worth two segmentations
The goal of image registration is to establish spatial correspondence between two or more images, traditionally through dense displacement fields (DDFs) or parametric transformations (e. g., rigid, affine, and splines).
A predictive machine learning force field framework for liquid electrolyte development
Despite the widespread applications of machine learning force fields (MLFF) in solids and small molecules, there is a notable gap in applying MLFF to simulate liquid electrolyte, a critical component of the current commercial lithium-ion battery.
Weakly supervised localisation of prostate cancer using reinforcement learning for bi-parametric MR images
The object-presence classifier may then inform the controller of its localisation quality by quantifying the likelihood of the image containing an object.
Semi-weakly-supervised neural network training for medical image registration
For training registration networks, weak supervision from segmented corresponding regions-of-interest (ROIs) have been proven effective for (a) supplementing unsupervised methods, and (b) being used independently in registration tasks in which unsupervised losses are unavailable or ineffective.
Combiner and HyperCombiner Networks: Rules to Combine Multimodality MR Images for Prostate Cancer Localisation
One of the distinct characteristics in radiologists' reading of multiparametric prostate MR scans, using reporting systems such as PI-RADS v2. 1, is to score individual types of MR modalities, T2-weighted, diffusion-weighted, and dynamic contrast-enhanced, and then combine these image-modality-specific scores using standardised decision rules to predict the likelihood of clinically significant cancer.
Machine Learning Force Fields with Data Cost Aware Training
To address this issue, we propose a multi-stage computational framework -- ASTEROID, which lowers the data cost of MLFFs by leveraging a combination of cheap inaccurate data and expensive accurate data.
Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusion
For the first time, we evaluate the realism of the generated pathology by blind expert identification of the presence of suspected lesions, where we find that the clinician performs similarly for both real and synthesised images, with a 2. 9 percentage point difference in lesion identification accuracy between real and synthesised images, demonstrating the potentials in radiological training purposes.
Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration
The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and expert annotations.
Cross-Modality Image Registration using a Training-Time Privileged Third Modality
In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additional modality that is different to those being registered.
The impact of using voxel-level segmentation metrics on evaluating multifocal prostate cancer localisation
However, the differences in false-positives and false-negatives, between the actual errors and the perceived counterparts if DSC is used, can be as high as 152 and 154, respectively, out of the 357 test set lesions.
Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation
This allows for the trained IQA controller to measure the impact an image has on the target task performance, when this task is performed using the predictor, e. g. segmentation and classification neural networks in modern clinical applications.
Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning
The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class are available from local healthcare providers, is sought-after.
Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR images
Prostate cancer (PCa) is one of the leading causes of death for men worldwide.
Unsupervised End-to-end Learning for Deformable Medical Image Registration
The contributions of our algorithm are threefold: (1) We transplant traditional image registration algorithms to an end-to-end convolutional neural network framework, while maintaining the unsupervised nature of image registration problems.
Flexibly imposing periodicity in kernel independent FMM: A Multipole-To-Local operator approach
An important but missing component in the application of the kernel independent fast multipole method (KIFMM) is the capability for flexibly and efficiently imposing singly, doubly, and triply periodic boundary conditions.
Numerical Analysis
