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Found 43 papers, 18 papers with code
Towards Universal Learning-based Model for Cardiac Image Reconstruction: Summary of the CMRxRecon2024 Challenge
In addition, through a detailed analysis of the results submitted to the challenge, we have also made several findings, including: 1) adaptive prompt-learning embedding is an effective means for achieving strong generalization in reconstruction models; 2) enhanced data consistency based on physics-informed networks is also an effective pathway toward a universal model; 3) traditional evaluation metrics have limitations when assessing ground-truth references with moderate or lower image quality, highlighting the need for subjective evaluation methods.
Current Pathology Foundation Models are unrobust to Medical Center Differences
We measure whether pathology FMs focus on biological features like tissue and cancer type, or on the well known confounding medical center signatures introduced by staining procedure and other differences.
ECTIL: Label-efficient Computational Tumour Infiltrating Lymphocyte (TIL) assessment in breast cancer: Multicentre validation in 2,340 patients with breast cancer
We propose and validate a fundamentally simpler deep learning based CTA that can be trained in only ten minutes on hundredfold fewer pathologist annotations.
Deep End-to-end Adaptive k-Space Sampling, Reconstruction, and Registration for Dynamic MRI
This is followed by a DL-based reconstruction module that produces images optimized for accurate deformation field estimation from the undersampled moving data.
Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxiliary Refinement Network
Cardiac MRI (CMRI) is a cornerstone imaging modality that provides in-depth insights into cardiac structure and function.
Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms
Precision breast cancer (BC) risk assessment is crucial for developing individualized screening and prevention.
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023
To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI.
End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRI
$\textbf{Methods:}$ We introduce the End-to-end Adaptive Dynamic Sampling and Reconstruction (E2E-ADS-Recon) for MRI framework, which integrates an adaptive dynamic sampler (ADS) that adapts the acquisition trajectory to each case for a given acceleration factor with a state-of-the-art dynamic reconstruction network, vSHARP, for reconstructing the adaptively sampled data into a dynamic image.
Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CT
Our method surpasses classical and deep learning baselines, including LIRE, on the thorax test set.
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection
We use a clinical dataset with 1630 MRI scans from 314 patients treated with NAC.
Nodule detection and generation on chest X-rays: NODE21 Challenge
To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays.
Task-Driven Wavelets using Constrained Empirical Risk Minimization
Deep Neural Networks (DNNs) are widely used for their ability to effectively approximate large classes of functions.
Joint Supervised and Self-supervised Learning for MRI Reconstruction
JSSL operates by simultaneously training a model in a self-supervised learning setting, using subsampled data from the target dataset(s), and in a supervised learning manner, utilizing datasets with fully-sampled $k$-space data, referred to as proxy datasets.
Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithms
If one only requires only marginal calibration on the image level, this calibration set consists of all individual pixels in the images available for calibration.
Deep Cardiac MRI Reconstruction with ADMM
In this work, inspired by related work in accelerated MRI reconstruction, we present a deep learning (DL)-based method for accelerated cine and multi-contrast reconstruction in the context of dynamic cardiac imaging.
vSHARP: variable Splitting Half-quadratic Admm algorithm for Reconstruction of inverse-Problems
In this study, we propose vSHARP (variable Splitting Half-quadratic ADMM algorithm for Reconstruction of inverse Problems), a novel DL-based method for solving ill-posed inverse problems arising in MI.
Improving Lesion Volume Measurements on Digital Mammograms
Finally, for a subset of 100 mammograms with a malign mass and concurrent MRI examination available, we analyze the agreement between lesion volume on mammography and MRI, resulting in an intraclass correlation coefficient of 0. 81 [95% CI 0. 73 - 0. 87] for consistency and 0. 78 [95% CI 0. 66 - 0. 86] for absolute agreement.
Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based Hierarchical Fusion Network with Attention Mechanism
DWIs with different b-values are fused to efficiently utilize the difference features of DWIs.
Constrained Empirical Risk Minimization: Theory and Practice
Deep Neural Networks (DNNs) are widely used for their ability to effectively approximate large classes of functions.
IMPORTANT-Net: Integrated MRI Multi-Parameter Reinforcement Fusion Generator with Attention Network for Synthesizing Absent Data
Then the multi-parameter fusion with attention module enables the interaction of the encoded information from different parameters through a set of algorithmic strategies, and applies different weights to the information through the attention mechanism after information fusion to obtain refined representation information.
Synthesis-based Imaging-Differentiation Representation Learning for Multi-Sequence 3D/4D MRI
Multi-sequence MRIs can be necessary for reliable diagnosis in clinical practice due to the complimentary information within sequences.
On Retrospective k-space Subsampling schemes For Deep MRI Reconstruction
This work investigates the impact of the $k$-space subsampling scheme on the quality of reconstructed accelerated MRI measurements produced by trained DL models.
FlowNet-PET: Unsupervised Learning to Perform Respiratory Motion Correction in PET Imaging
To correct for respiratory motion in PET imaging, an interpretable and unsupervised deep learning technique, FlowNet-PET, was constructed.
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction
Magnetic Resonance Imaging can produce detailed images of the anatomy and physiology of the human body that can assist doctors in diagnosing and treating pathologies such as tumours.
Subpixel object segmentation using wavelets and multi resolution analysis
The effectiveness of the proposed method is demonstrated by delineating boundaries of simply connected domains (organs) in medical images using Debauches wavelets and comparing performance with a U-Net baseline.
Subpixel object segmentation using wavelets and multiresolution analysis
The effectiveness of the proposed method is demonstrated by delineating boundaries of simply connected domains (organs) in medical images using Debauches wavelets and comparing performance with a U-Net baseline.
WeakSTIL: Weak whole-slide image level stromal tumor infiltrating lymphocyte scores are all you need
We present WeakSTIL, an interpretable two-stage weak label deep learning pipeline for scoring the percentage of stromal tumor infiltrating lymphocytes (sTIL%) in H&E-stained whole-slide images (WSIs) of breast cancer tissue.
Deep MRI Reconstruction with Radial Subsampling
In spite of its extensive adaptation in almost every medical diagnostic and examinatorial application, Magnetic Resonance Imaging (MRI) is still a slow imaging modality which limits its use for dynamic imaging.
DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer
For MSI prediction in a tumor-annotated and color normalized subset of TCGA-CRC (n=360 patients), contrastive self-supervised learning improves the tile supervision baseline from 0. 77 to 0. 87 AUROC, on par with our proposed DeepSMILE method.
Sparse-shot Learning with Exclusive Cross-Entropy for Extremely Many Localisations
However, outside the setting of regular images, we are often confronted with a different situation.
Automatic Breast Lesion Detection in Ultrafast DCE-MRI Using Deep Learning
Purpose: We propose a deep learning-based computer-aided detection (CADe) method to detect breast lesions in ultrafast DCE-MRI sequences.
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community.
Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process.
Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation
This study proposes a reconstruction algorithm for DBT based on deep learning specifically optimized for these tasks.
Kernel of CycleGAN as a principal homogeneous space
It is known that the CycleGAN problem might admit multiple solutions, and our goal in this paper is to analyze the space of exact solutions and to give perturbation bounds for approximate solutions.
Kernel of CycleGAN as a Principle homogeneous space
It is known that the CycleGAN problem might admit multiple solutions, and our goal in this paper is to analyze the space of exact solutions and to give perturbation bounds for approximate solutions.
i-RIM applied to the fastMRI challenge
We, team AImsterdam, summarize our submission to the fastMRI challenge (Zbontar et al., 2018).
Improving Breast Cancer Detection using Symmetry Information with Deep Learning
At candidate level, AUC value of 0. 933 with 95% confidence interval of [0. 920, 0. 954] was obtained when symmetry information is incorporated in comparison with baseline architecture which yielded AUC value of 0. 929 with [0. 919, 0. 947] confidence interval.
Deep Learning Framework for Digital Breast Tomosynthesis Reconstruction
In this study we propose an extension of the Learned Primal-Dual algorithm for digital breast tomosynthesis.
Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation
We analyze two transfer learning settings: 1) unsupervised transfer, where Hologic data with soft lesion annotation at pixel level and Siemens unlabelled data are used to annotate images in the latter data; 2) weak supervised transfer, where exam level labels for images from the Siemens mammograph are available.
Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network
In conclusion, the method could be used as a candidate selection model with high accuracy and with the additional information of lesion segmentation.
Student Beats the Teacher: Deep Neural Networks for Lateral Ventricles Segmentation in Brain MR
To show this, we use noisy segmentation labels generated by a conventional region growing algorithm to train a deep network for lateral ventricle segmentation.
