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Found 24 papers, 9 papers with code
VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledge
In contrast, we propose that for medical VLMs, a fourth stage of specialized IFT is necessary, which focuses on medical data and includes information from domain expert models.
MAISI: Medical AI for Synthetic Imaging
Medical imaging analysis faces challenges such as data scarcity, high annotation costs, and privacy concerns.
Location-based Radiology Report-Guided Semi-supervised Learning for Prostate Cancer Detection
Prostate cancer is one of the most prevalent malignancies in the world.
VISTA3D: Versatile Imaging SegmenTation and Annotation model for 3D Computed Tomography
The novel model design and training recipe represent a promising step toward developing a versatile medical image foundation model and will serve as a valuable foundation for CT image analysis.
Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning
We also collected CT scans of 1, 350 patients from publicly available sources for benchmarking purposes.
Detection of Peri-Pancreatic Edema using Deep Learning and Radiomics Techniques
Identifying peri-pancreatic edema is a pivotal indicator for identifying disease progression and prognosis, emphasizing the critical need for accurate detection and assessment in pancreatitis diagnosis and management.
A Probabilistic Hadamard U-Net for MRI Bias Field Correction
Random samples drawn from latent space are then incorporated with a prototypical corrected image to generate multiple plausible images.
Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging
The best primary model was then used to identify tumors in the remaining 861 patients and bounding box coordinates were generated on their scans using the model.
GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification
To our best knowledge, GazeGNN is the first work that adopts GNN to integrate image and eye-gaze data.
Automatic segmentation of clear cell renal cell tumors, kidney, and cysts in patients with von Hippel-Lindau syndrome using U-net architecture on magnetic resonance images
Our 2D U-Net achieved an average ccRCC lesion detection Area under the curve (AUC) of 0. 88 and DSC scores of 0. 78, 0. 40, and 0. 46 for segmentation of the kidney, cysts, and tumors, respectively.
Distance Map Supervised Landmark Localization for MR-TRUS Registration
We then use the predicted landmarks to generate the affine transformation matrix, which outperforms the clinicians' manual rigid registration by a significant margin in terms of TRE.
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation
Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing.
Cross-modal Attention for MRI and Ultrasound Volume Registration
In the past few years, convolutional neural networks (CNNs) have been proved powerful in extracting image features crucial for image registration.
Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation
In this work, we design a new data-driven approach, namely Auto-FedAvg, where aggregation weights are dynamically adjusted, depending on data distributions across data silos and the current training progress of the models.
Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis
We provide an innovative visual explanation algorithm for general purpose and as an example application, we demonstrate its effectiveness for quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels.
Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis, which has shown promising results.
Multi-Domain Image Completion for Random Missing Input Data
Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e. g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI).
Adipose Tissue Segmentation in Unlabeled Abdomen MRI using Cross Modality Domain Adaptation
Abdominal fat quantification is critical since multiple vital organs are located within this region.
Hybrid Apparent Diffusion Coefficient (HADC) Map
This work can considerably improve the quality of mpMRI combined assessment for prostate cancer detection.
Multi-view Framework for Histomorphologic Classification
Current routine histopathologic evaluation of prostate cancer does not fully account for some individual morphology patterns associated with poor outcome.
When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation
We rethink data augmentation for medical 3D images and propose a deep stacked transformations (DST) approach for domain generalization.
A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning
Computer aided diagnostic (CAD) tools are developed to help radiologists to compensate for some of these errors.
Deeply-Supervised CNN for Prostate Segmentation
Prostate segmentation from Magnetic Resonance (MR) images plays an important role in image guided interven- tion.
Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation
During diagnostic assessment of lung CT scans, the radiologists' gaze information were used to create a visual attention map.
