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%
- instance of Ultrasound : journal of the British Medical Ultrasound Society 70%
- used by X-ray 70%
- competes with magnetic resonance imaging 60%
- instance of Mauritius 60%
- used by Mauritius 60%
- used by magnetic resonance imaging 50%
- instance of deep learning 50%
- competes with Ultrasound 50%
15 day(s) with sentiment data
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Withdrawn paper proposes novel 3D BIT for medical imaging volume computation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,…
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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…
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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…
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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.…
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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…
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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…
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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…
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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 …
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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…
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Handling Missing Modalities in Multimodal Survival Prediction for Non-Small Cell Lung Cancer
Researchers have developed a novel multimodal deep learning framework designed to improve survival prediction for Non-Small Cell Lung Cancer (NSCLC). This framework effectively handles missing data across clinical, radi…
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Video models show zero-shot learning and reasoning in medical imaging
A new research paper explores the potential of large video models (LVMs) to perform zero-shot learning and reasoning in medical imaging. Researchers evaluated an LVM on tasks like organ segmentation, denoising, super-re…
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An Artifact-based Agent Framework for Adaptive and Reproducible Medical Image Processing
Researchers have developed a new framework called SPD to improve the accuracy of medical image segmentation using foundation models like SAM. SPD addresses the issue of noisy and imprecise prompts, which are common in c…
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Diffusion models enhance image reconstruction for inverse problems and sparse-view CT
Researchers are developing new methods to improve image reconstruction from limited data using diffusion models. One approach optimizes diffusion priors from a single observation by combining existing models, showing pr…
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New frameworks enhance text-guided 3D medical image segmentation accuracy
Researchers have developed new methods for text-guided 3D medical image segmentation, aiming to improve precision in analyzing scans like MRIs. One approach, "Align then Refine," uses a multi-encoder U-Net with alignmen…