AI Medical Compendium Journal:
Medical image analysis

Showing 71 to 80 of 684 articles

Style mixup enhanced disentanglement learning for unsupervised domain adaptation in medical image segmentation.

Medical image analysis
Unsupervised domain adaptation (UDA) has shown impressive performance by improving the generalizability of the model to tackle the domain shift problem for cross-modality medical segmentation. However, most of the existing UDA approaches depend on hi...

Learnable color space conversion and fusion for stain normalization in pathology images.

Medical image analysis
Variations in hue and contrast are common in H&E-stained pathology images due to differences in slide preparation across various institutions. Such stain variations, while not affecting pathologists much in diagnosing the biopsy, pose significant cha...

SurgiTrack: Fine-grained multi-class multi-tool tracking in surgical videos.

Medical image analysis
Accurate tool tracking is essential for the success of computer-assisted intervention. Previous efforts often modeled tool trajectories rigidly, overlooking the dynamic nature of surgical procedures, especially tracking scenarios like out-of-body and...

Personalized dental crown design: A point-to-mesh completion network.

Medical image analysis
Designing dental crowns with computer-aided design software in dental laboratories is complex and time-consuming. Using real clinical datasets, we developed an end-to-end deep learning model that automatically generates personalized dental crown mesh...

Brain networks and intelligence: A graph neural network based approach to resting state fMRI data.

Medical image analysis
Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying...

Personalized predictions of Glioblastoma infiltration: Mathematical models, Physics-Informed Neural Networks and multimodal scans.

Medical image analysis
Predicting the infiltration of Glioblastoma (GBM) from medical MRI scans is crucial for understanding tumor growth dynamics and designing personalized radiotherapy treatment plans. Mathematical models of GBM growth can complement the data in the pred...

Contrastive machine learning reveals species -shared and -specific brain functional architecture.

Medical image analysis
A deep comparative analysis of brain functional connectome across species in primates has the potential to yield valuable insights for both scientific and clinical applications. However, the interspecies commonality and differences are inherently ent...

MoMA: Momentum contrastive learning with multi-head attention-based knowledge distillation for histopathology image analysis.

Medical image analysis
There is no doubt that advanced artificial intelligence models and high quality data are the keys to success in developing computational pathology tools. Although the overall volume of pathology data keeps increasing, a lack of quality data is a comm...

Dual-modality visual feature flow for medical report generation.

Medical image analysis
Medical report generation, a cross-modal task of generating medical text information, aiming to provide professional descriptions of medical images in clinical language. Despite some methods have made progress, there are still some limitations, inclu...

Toward automated detection of microbleeds with anatomical scale localization using deep learning.

Medical image analysis
Cerebral Microbleeds (CMBs) are chronic deposits of small blood products in the brain tissues, which have explicit relation to various cerebrovascular diseases depending on their anatomical location, including cognitive decline, intracerebral hemorrh...