Tim Leiner
@MLandDL_papers
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This is a twitter feed with #machinelearning and #deeplearning literature from PubMed and arXiv relevant to medical imaging and #radiology
Utrecht, The Netherlands
Joined June 2018
Detecting whether L1 or other lumbar levels would be excluded from DXA bone mineral density analysis during opportunistic CT screening for osteoporosis using machine learning
pubmed.ncbi.nlm.nih.gov
Machine learning algorithms could be used to identify which lumbar vertebrae would be excluded from DXA analysis and should not be used for opportunistic CT screening analysis. The SVM was better...
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Neural network algorithm for detection of erosions and ankylosis on CT of the sacroiliac joints: multicentre development and validation of diagnostic accuracy
pubmed.ncbi.nlm.nih.gov
• Structural lesions of sacroiliitis can be detected automatically in pelvic CT scans. • Both automatic segmentation and disease detection yield excellent statistical outcome metrics. • The algorithm...
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AI-assisted compressed sensing and parallel imaging sequences for MRI of patients with nasopharyngeal carcinoma: comparison of their capabilities in terms of examination time and image quality
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Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network
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Computed Tomography Urography: State of the Art and Beyond
pubmed.ncbi.nlm.nih.gov
Computed Tomography Urography (CTU) is a multiphase CT examination optimized for imaging kidneys, ureters, and bladder, complemented by post-contrast excretory phase imaging. Different protocols are...
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Comparison of Pulmonary Congestion severity using AI-assisted scoring vs. clinical experts: A Secondary Analysis of BLUSHED-AHF
pubmed.ncbi.nlm.nih.gov
Artificial intelligence/machine learning-based LCS correlated with expert-level B-line quantification. Future studies are needed to determine whether automated tools may assist novice users in LUS...
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A survey on automatic generation of medical imaging reports based on deep learning
pubmed.ncbi.nlm.nih.gov
Recent advances in deep learning have shown great potential for the automatic generation of medical imaging reports. Deep learning techniques, inspired by image captioning, have made significant...
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SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models. (arXiv:2305.11281v1 [)
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JoIN: Joint GANs Inversion for Intrinsic Image Decomposition. (arXiv:2305.11321v1 [)
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MALM: Mask Augmentation based Local Matching for Food-Recipe Retrieval. (arXiv:2305.11327v1 [)
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RoomDreamer: Text-Driven 3D Indoor Scene Synthesis with Coherent Geometry and Texture. (arXiv:2305.11337v1 [)
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Coordinated Transformer with Position \& Sample-aware Central Loss for Anatomical Landmark Detection. (arXiv:2305.11338v1 [)
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Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoning. (arXiv:2305.11347v1 [)
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Data Redaction from Conditional Generative Models. (arXiv:2305.11351v1 [cs.LG])
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Enhancing Transformer Backbone for Egocentric Video Action Segmentation. (arXiv:2305.11365v1 [)
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Smart Pressure e-Mat for Human Sleeping Posture and Dynamic Activity Recognition. (arXiv:2305.11367v1 [)
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Deep Image Compression Using Scene Text Quality Assessment. (arXiv:2305.11373v1 [)
arxiv.org
Image compression is a fundamental technology for Internet communication engineering. However, a high compression rate with general methods may degrade images, resulting in unreadable texts. In...
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Fast-StrucTexT: An Efficient Hourglass Transformer with Modality-guided Dynamic Token Merge for Document Understanding. (arXiv:2305.11392v1 [)
arxiv.org
Transformers achieve promising performance in document understanding because of their high effectiveness and still suffer from quadratic computational complexity dependency on the sequence length....
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Remembering What Is Important: A Factorised Multi-Head Retrieval and Auxiliary Memory Stabilisation Scheme for Human Motion Prediction. (arXiv:2305.11394v1 [)
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Efficient Mixed Transformer for Single Image Super-Resolution. (arXiv:2305.11403v1 [)
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