computed tomography
PulseAugur coverage of computed tomography — every cluster mentioning computed tomography across labs, papers, and developer communities, ranked by signal.
- used by Polyethylene Terephthalate 70%
- used by deep learning 70%
- used by X-ray 70%
- competes with magnetic resonance imaging 60%
- used by Mauritius 60%
- used by magnetic resonance imaging 50%
- instance of Ultrasound : journal of the British Medical Ultrasound Society 50%
- competes with Ultrasound 50%
- instance of deep learning 50%
10 天有情绪数据
-
AI optimizes surgical planning for enhanced bone union in mandibular reconstruction
Researchers have developed OsteoOpt++, a novel image-to-decision planning loop designed to enhance bone union in mandibular reconstruction surgeries. This system creates a personalized digital twin from pre-operative CT…
-
New self-supervised methods improve low-dose CT denoising for medical imaging
Researchers have developed new self-supervised learning methods for denoising low-dose CT scans, a crucial step for reducing radiation exposure in medical imaging. One approach, Progressive $\mathcal{J}$-Invariant Learn…
-
Researchers develop region-adaptive AI for enhanced CT image reconstruction
Researchers have developed RA-CMF, a novel conditional MeanFlow pipeline for CT image reconstruction that enhances image quality for cancer diagnosis. The system uses a conditional MeanFlow network to predict image-cond…
-
New augmentation technique boosts medical image segmentation across CT and MRI
Researchers have developed a novel data augmentation technique to improve the cross-modality generalization of deep learning models for 3D spine segmentation in medical imaging. This approach significantly boosts perfor…
-
AI generates synthetic PET scans to improve lung cancer histology classification
Researchers have developed a novel framework using a 3D Pix2Pix Generative Adversarial Network (GAN) to create synthetic PET scans from CT data for non-small cell lung cancer (NSCLC) histology classification. This "virt…
-
New Gated Differential Linear Attention boosts medical image segmentation accuracy
Researchers have developed a new Gated Differential Linear Attention (GDLA) mechanism designed to improve medical image segmentation. This approach combines the efficiency of linear attention with enhanced boundary pres…
-
Mayo Clinic AI model detects pancreatic cancer up to three years earlier
Mayo Clinic has developed an AI model named REDMOD that can detect pancreatic cancer on routine CT scans up to three years earlier than current methods. The model analyzes hundreds of imaging features to identify subtle…
-
Deep learning models segment peritoneal cancer regions in CT scans
Researchers have developed a deep learning method to automatically segment regions for the radiological Peritoneal Cancer Index (rPCI) from CT scans. The study evaluated nnU-Net and Swin UNETR on 62 CT scans, with nnU-N…
-
Zuckerberg's Biohub commits $500M to AI biology, Mayo Clinic AI spots cancer early
Mark Zuckerberg and Priscilla Chan's Biohub has launched a $500 million Virtual Biology Initiative aimed at accelerating AI's role in understanding and combating disease. The five-year project will focus on generating v…
-
Simple MIL matches complex models for 3D neuroimage classification
Researchers have published a benchmark comparing multiple instance learning (MIL) methods against 3D CNNs and ViTs for classifying 3D neuroimages. The study found that a simple mean pooling MIL approach, without attenti…
-
AI and VR create patient-specific surgical simulations from medical scans
Researchers have developed a novel system that uses AI and computer vision to create patient-specific virtual reality simulations for spine surgery training. This platform automates the generation of 3D anatomical model…
-
Withdrawn paper proposes novel 3D BIT for medical imaging volume computation
A research paper introduced a novel algorithm for accurately computing the volume of 3D reconstructed models from medical imaging data, such as CT and MR scans. The method combines calculus, the marching cube algorithm,…
-
CRC-SAM framework enables multi-modal colorectal cancer segmentation
Researchers have developed CRC-SAM, a novel framework for segmenting colorectal cancer across multiple imaging types including CT, colonoscopy, and histology. This system builds upon the MedSAM model and utilizes low-ra…
-
SAM model shows stable spleen segmentation in CT scans despite domain shifts
Researchers evaluated the robustness of the Segment Anything Model (SAM) for spleen segmentation in abdominal CT scans, simulating various domain shifts like noise and resolution changes. The study found that SAM mainta…
-
Alibaba AI model surpasses radiologists in early colorectal cancer detection
Alibaba has developed an AI model capable of detecting early-stage colorectal cancer from CT scans with higher accuracy than human radiologists. In clinical trials involving over 27,000 scans, the model demonstrated 86.…
-
DeepSeek AI releases major update, Anthropic blocks company, Alibaba unveils cancer AI
Alibaba's DAMO Academy has developed a new AI model, DAMO COCA, designed for opportunistic colorectal cancer screening using CT scans. This model achieved high sensitivity and specificity in identifying missed cases fro…
-
AI research tackles CT scan bias and accuracy with KL-regularised Group DRO
Researchers have developed a new method called KL-Regularised Group Distributionally Robust Optimisation (Group DRO) to improve the fairness and robustness of AI models used for classifying volumetric CT scans. This app…
-
New CT-guided regularization improves whole-body PET registration accuracy
Researchers have developed a new method for aligning whole-body PET scans, crucial for tracking cancer progression. Their approach uses CT scans to guide the deformation process, applying stronger regularization to rigi…
-
New AI model improves pediatric PET scans by removing CT radiation
Researchers have developed a novel dual-domain network called the Generalizable PET Correction Network (GPCN) to improve CT-free PET imaging for pediatric patients. This network aims to provide accurate attenuation and …
-
EXACT model offers explainable anomaly detection for 3D chest CT scans
Researchers have developed EXACT, a novel foundation model designed for analyzing 3D chest CT scans. This model learns spatially resolved representations from paired CT scans and radiology reports, enabling it to not on…