AIMC Topic: Deep Learning

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Impact of Transfer Learning Using Local Data on Performance of a Deep Learning Model for Screening Mammography.

Radiology. Artificial intelligence
Purpose To investigate the issues of generalizability and replication of deep learning models by assessing performance of a screening mammography deep learning system developed at New York University (NYU) on a local Australian dataset. Materials and...

Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review.

Radiology. Artificial intelligence
Purpose To investigate the accuracy and robustness of prostate segmentation using deep learning across various training data sizes, MRI vendors, prostate zones, and testing methods relative to fellowship-trained diagnostic radiologists. Materials and...

DP2LM: leveraging deep learning approach for estimation and hypothesis testing on mediation effects with high-dimensional mediators and complex confounders.

Biostatistics (Oxford, England)
Traditional linear mediation analysis has inherent limitations when it comes to handling high-dimensional mediators. Particularly, accurately estimating and rigorously inferring mediation effects is challenging, primarily due to the intertwined natur...

LinFlo-Net: A Two-Stage Deep Learning Method to Generate Simulation Ready Meshes of the Heart.

Journal of biomechanical engineering
We present a deep learning model to automatically generate computer models of the human heart from patient imaging data with an emphasis on its capability to generate thin-walled cardiac structures. Our method works by deforming a template mesh to fi...

A Deep Learning-Based Approach to Estimate Paneth Cell Granule Area in Celiac Disease.

Archives of pathology & laboratory medicine
CONTEXT.—: Changes in Paneth cell numbers can be associated with chronic inflammatory diseases of the gastrointestinal tract. So far, no consensus has been achieved on the number of Paneth cells and their relevance to celiac disease (CD).

TA-RNN: an attention-based time-aware recurrent neural network architecture for electronic health records.

Bioinformatics (Oxford, England)
MOTIVATION: Electronic health records (EHRs) represent a comprehensive resource of a patient's medical history. EHRs are essential for utilizing advanced technologies such as deep learning (DL), enabling healthcare providers to analyze extensive data...

MolLM: a unified language model for integrating biomedical text with 2D and 3D molecular representations.

Bioinformatics (Oxford, England)
MOTIVATION: The current paradigm of deep learning models for the joint representation of molecules and text primarily relies on 1D or 2D molecular formats, neglecting significant 3D structural information that offers valuable physical insight. This n...

CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations.

Bioinformatics (Oxford, England)
MOTIVATION: High-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell lines. Their ability to evaluate a wide spectrum of interventions, from single drugs to intricate dr...