Results 81 to 90 of about 13,515,348 (328)
Medical image data augmentation: techniques, comparisons and interpretations
Artificial Intelligence Review, 2023 Designing deep learning based methods with medical images has always been an attractive area of research to assist clinicians in rapid examination and accurate diagnosis.
Evgin Göçeri
semanticscholar +1 more source
Automatic COVID-19 and Common-Acquired Pneumonia Diagnosis Using Chest CT Scans
Bioengineering, 2023 Even with over 80% of the population being vaccinated against COVID-19, the disease continues to claim victims. Therefore, it is crucial to have a secure Computer-Aided Diagnostic system that can assist in identifying COVID-19 and determining the ...
Pedro Crosara Motta +4 more
doaj +1 more source
Denoising diffusion probabilistic models for 3D medical image generation
Scientific Reports, 2023 Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models have generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen, and Stable Diffusion.
Firas Khader +14 more
semanticscholar +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT A second allogeneic (allo‐)hematopoietic stem cell transplantation (HSCT2) is a potential curative option for pediatric patients with acute lymphoblastic leukemia (ALL) following relapse after first allogeneic transplantation (HSCT1), but its efficacy is limited by high relapse rates and transplant‐related toxicity in highly pretreated ...
Ava Momm +10 more
wiley +1 more source
Pediatric Blood &Cancer, EarlyView.Abstract Background A routine baseline echocardiogram is often obtained prior to anthracycline administration in children with cancer. The utility of baseline echocardiogram is unclear in patients with standard risk B‐cell acute lymphoblastic leukemia (SR B‐ALL) as their anthracycline cumulative dose is low.
Ziyad Alrajhi +4 more
wiley +1 more source
Attention Guided Policy Optimization for 3D Medical Image Registration
IEEE Access, 2023 Learning-based image registration approaches typically learn to map from input images to a transformation matrix. Regarding the current deep-learning-based image rigid registration approaches learn a transformation matrix in a one-shot way.
Jing Hu +6 more
doaj +1 more source
A Latent Source Model for Patch-Based Image Segmentation
, 2015 Despite the popularity and empirical success of patch-based nearest-neighbor and weighted majority voting approaches to medical image segmentation, there has been no theoretical development on when, why, and how well these nonparametric methods work.
Chen, George +2 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Although most malignant germ cell tumors (GCTs) are highly curable with cisplatin‐based therapy, options for patients with multiply relapsed/refractory disease remain limited. For this population, we report the first pediatric use of gemcitabine, docetaxel, melphalan, and carboplatin (GemDMC) as part of sequential cycles of high‐dose ...
Maria Frost +10 more
wiley +1 more source
Frontiers in Neuroscience, 2021 Diabetes with high blood glucose levels may damage the brain nerves and thus increase the risk of dementia. Previous studies have shown that dementia can be reflected in altered brain structure, facilitating computer-aided diagnosis of brain diseases ...
Yuna Chen +14 more
doaj +1 more source
The Influence of Intensity Standardization on Medical Image Registration
, 2010 Acquisition-to-acquisition signal intensity variations (non-standardness) are inherent in MR images. Standardization is a post processing method for correcting inter-subject intensity variations through transforming all images from the given image gray ...
Bagci, Ulas, Bai, Li, Udupa, Jayaram K.
core +1 more source