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Found 17 papers, 5 papers with code
Automatic recognition of abdominal lymph nodes from clinical text
We then introduce an end-to-end approach based on the combination of rules and transformer-based methods to detect these abdominal lymph node mentions and classify their types from the MRI radiology reports.
Lymph Node Detection in T2 MRI with Transformers
Identification of lymph nodes (LN) in T2 Magnetic Resonance Imaging (MRI) is an important step performed by radiologists during the assessment of lymphoproliferative diseases.
Induction, Popper, and machine learning
Moreover, at a more meta level the process of development of all AI algorithms can be understood under the framework of universal Darwinism.
Applying Deutsch's concept of good explanations to artificial intelligence and neuroscience -- an initial exploration
We argue that figuring out how replicate this second system, which is capable of generating hard-to-vary explanations, is a key challenge which needs to be solved in order to realize artificial general intelligence.
Deep Small Bowel Segmentation with Cylindrical Topological Constraints
Since the inner cylinder is free of the touching issue, a cylindrical shape constraint applied on this augmented branch guides the network to generate a topologically correct segmentation.
Cross-Domain Medical Image Translation by Shared Latent Gaussian Mixture Model
We expect the utility of our framework will extend to other problems beyond segmentation due to the improved quality of the generated images and enhanced ability to preserve small structures.
Image Translation by Latent Union of Subspaces for Cross-Domain Plaque Detection
In medical imaging applications, preserving small structures is important since these structures can carry information which is highly relevant for disease diagnosis.
Self-explaining AI as an alternative to interpretable AI
While it is often possible to approximate the input-output relations of deep neural networks with a few human-understandable rules, the discovery of the double descent phenomena suggests that such approximations do not accurately capture the mechanism by which deep neural networks work.
Accurately identifying vertebral levels in large datasets
We next developed an algorithm which performs iterative instance segmentation and classification of the entire spine with a 3D U-Net.
Image Translation by Latent Union of Subspaces for Cross-Domain Plaque Segmentation
In medical imaging applications, preserving small structures is important since these structures can carry information which is highly relevant for disease diagnosis.
Phonon Lifetimes and Thermal Conductivity of the Molecular Crystal $α$-RDX
This is likely because diffusive carriers contribute to over 95% of the thermal conductivity in ${\alpha}$-RDX.
Materials Science
Deep learning for molecular design - a review of the state of the art
In the space of only a few years, deep generative modeling has revolutionized how we think of artificial creativity, yielding autonomous systems which produce original images, music, and text.
Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora
The number of scientific journal articles and reports being published about energetic materials every year is growing exponentially, and therefore extracting relevant information and actionable insights from the latest research is becoming a considerable challenge.
Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning
Due to its high computational speed and accuracy compared to ab-initio quantum chemistry and forcefield modeling, the prediction of molecular properties using machine learning has received great attention in the fields of materials design and drug discovery.
A Phonon Boltzmann Study of Microscale Thermal Transport in $α$-RDX Cook-Off
The microscale thermal transport properties of $\alpha$RDX are believed to be major factors in the initiation process.
Materials Science
Machine Learning of Energetic Material Properties
In this work, we discuss use of machine learning techniques for rapid prediction of detonation properties including explosive energy, detonation velocity, and detonation pressure.
Materials Science Chemical Physics Computational Physics
Using a monomer potential energy surface to perform approximate path integral molecular dynamics simulation of ab-initio water with near-zero added cost
We show that our method, which we call "monomer PIMD", correctly captures changes in the structure of water found in a full PIMD simulation but at much lower computational cost.
Chemical Physics Soft Condensed Matter Computational Physics
