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Found 14 papers, 6 papers with code
Scalable Drift Monitoring in Medical Imaging AI
By emphasizing the importance of monitoring diverse data streams and evaluating data shifts alongside model performance, this work contributes to the broader adoption and integration of AI solutions in clinical settings.
Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathology
The interactions between tumor cells and the tumor microenvironment (TME) dictate therapeutic efficacy of radiation and many systemic therapies in breast cancer.
Automatic classification of prostate MR series type using image content and metadata
With the wealth of medical image data, efficient curation is essential.
PRECISE Framework: GPT-based Text For Improved Readability, Reliability, and Understandability of Radiology Reports For Patient-Centered Care
This study introduces and evaluates the PRECISE framework, utilizing OpenAI's GPT-4 to enhance patient engagement by providing clearer and more accessible chest X-ray reports at a sixth-grade reading level.
Is Open-Source There Yet? A Comparative Study on Commercial and Open-Source LLMs in Their Ability to Label Chest X-Ray Reports
While recent publications have explored GPT-4 in its application to extracting information of interest from radiology reports, there has not been a real-world comparison of GPT-4 to different leading open-source models.
A generalized framework to predict continuous scores from medical ordinal labels
These labels are used to train and evaluate disease severity prediction models.
Improving the repeatability of deep learning models with Monte Carlo dropout
During model development and evaluation, much attention is given to classification performance while model repeatability is rarely assessed, leading to the development of models that are unusable in clinical practice.
Monte Carlo dropout increases model repeatability
Leveraging Monte Carlo predictions significantly increased repeatability for all tasks on the binary, multi-class, and ordinal models leading to an average reduction of the 95% limits of agreement by 17% points.
Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology
The highdicom library ties into the extensive Python ecosystem for image processing and machine learning.
Addressing catastrophic forgetting for medical domain expansion
Notably, this approach degrades model performance at the original institution, a phenomenon known as catastrophic forgetting.
"Name that manufacturer". Relating image acquisition bias with task complexity when training deep learning models: experiments on head CT
Recognition of such bias is critical to develop robust, generalizable models that will be crucial for clinical applications in real-world data distributions.
Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks
The amounts of muscle and fat in a person's body, known as body composition, are correlated with cancer risks, cancer survival, and cardiovascular risk.
Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video
We present an automatic method to describe clinically useful information about scanning, and to guide image interpretation in ultrasound (US) videos of the fetal heart.
Introduction To The Monogenic Signal
The monogenic signal is an image analysis methodology that was introduced by Felsberg and Sommer in 2001 and has been employed for a variety of purposes in image processing and computer vision research.
