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Found 8 papers, 2 papers with code
Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains
Large Language Models (LLMs) have demonstrated remarkable proficiency in understanding and generating natural language.
RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models
Deep learning techniques, despite their potential, often suffer from a lack of reproducibility and generalizability, impeding their clinical adoption.
Improving mitosis detection on histopathology images using large vision-language models
In certain types of cancerous tissue, mitotic count has been shown to be associated with tumor proliferation, poor prognosis, and therapeutic resistance.
Virchow: A Million-Slide Digital Pathology Foundation Model
The use of artificial intelligence to enable precision medicine and decision support systems through the analysis of pathology images has the potential to revolutionize the diagnosis and treatment of cancer.
Best Practices and Scoring System on Reviewing A.I. based Medical Imaging Papers: Part 1 Classification
The goal of this series is to provide resources to not only help improve the review process for A. I.-based medical imaging papers, but to facilitate a standard for the information that is presented within all components of the research study.
Initialization and Regularization of Factorized Neural Layers
In model compression, we show that they enable low-rank methods to significantly outperform both unstructured sparsity and tensor methods on the task of training low-memory residual networks; analogs of the schemes also improve the performance of tensor decomposition techniques.
4D CNN for semantic segmentation of cardiac volumetric sequences
We propose a 4D convolutional neural network (CNN) for the segmentation of retrospective ECG-gated cardiac CT, a series of single-channel volumetric data over time.
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.
