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
-
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…
-
视频模型在医学影像中展现零样本学习和推理能力
一项新的研究论文探讨了大型视频模型（LVMs）在医学影像中执行零样本学习和推理的潜力。研究人员使用来自122名患者的4D CT扫描，在器官分割、去噪、超分辨率和运动预测等任务上评估了一个LVM。该模型表现出令人印象深刻的能力，在没有任何医学特定微调的情况下取得了有竞争力的性能，甚至在运动预测方面超越了专门的基线模型。
-
An Artifact-based Agent Framework for Adaptive and Reproducible Medical Image Processing
研究人员开发了一个名为SPD的新框架，利用SAM等基础模型来提高医学图像分割的准确性。SPD通过学习解剖学先验知识并利用相邻切片的上下文来改进引导，从而解决了临床环境中常见的提示词噪声大和不精确的问题。该方法旨在通过模仿专家推理并确保局部解剖学的一致性，使基础模型在临床诊断和监测方面更加可靠。在MRI和CT数据上的实验表明，SPD的性能优于现有方法和监督基线。
-
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…
-
新框架提升文本引导的3D医学图像分割精度
研究人员开发了新的文本引导3D医学图像分割方法，旨在提高分析MRI等扫描的精度。一种方法“Align then Refine”采用多编码器U-Net，结合对齐和热图损失来注入病变语义并优化边界。另一个框架ESICA提供了一个可扩展且计算效率高的解决方案，具有新颖的掩码预测公式和分解解码器，在多样化基准测试中取得了最先进的结果。