AIMC Topic: Deep Learning

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Automated design of multi-target ligands by generative deep learning.

Nature communications
Generative deep learning models enable data-driven de novo design of molecules with tailored features. Chemical language models (CLM) trained on string representations of molecules such as SMILES have been successfully employed to design new chemical...

A deep learning model for differentiating paediatric intracranial germ cell tumour subtypes and predicting survival with MRI: a multicentre prospective study.

BMC medicine
BACKGROUND: The pretherapeutic differentiation of subtypes of primary intracranial germ cell tumours (iGCTs), including germinomas (GEs) and nongerminomatous germ cell tumours (NGGCTs), is essential for clinical practice because of distinct treatment...

Multimodal radiomics-based methods using deep learning for prediction of brain metastasis in non-small cell lung cancer withF-FDG PET/CT images.

Biomedical physics & engineering express
. Approximately 57% of non-small cell lung cancer (NSCLC) patients face a 20% risk of brain metastases (BMs). The delivery of drugs to the central nervous system is challenging because of the blood-brain barrier, leading to a relatively poor prognosi...

Development and Validation of a Deep Learning Model for Prediction of Adult Physiological Deterioration.

Critical care explorations
BACKGROUND: Prediction-based strategies for physiologic deterioration offer the potential for earlier clinical interventions that improve patient outcomes. Current strategies are limited because they operate on inconsistent definitions of deteriorati...

Diagnostic Value of Magnetic Resonance Imaging Radiomics and Machine-learning in Grading Soft Tissue Sarcoma: A Mini-review on the Current State.

Academic radiology
Soft tissue sarcomas (STS) are a heterogeneous group of rare malignant tumors. Tumor grade might be underestimated in biopsy due to intratumoral heterogeneity. This mini-review aims to present the current state of predicting malignancy grades of STS ...

Applying deep learning-based ensemble model to [F]-FDG-PET-radiomic features for differentiating benign from malignant parotid gland diseases.

Japanese journal of radiology
OBJECTIVES: To develop and identify machine learning (ML) models using pretreatment 2-deoxy-2-[F]fluoro-D-glucose ([F]-FDG)-positron emission tomography (PET)-based radiomic features to differentiate benign from malignant parotid gland diseases (PGDs...

A Framework for Measuring Tree Rings Based on Panchromatic Images and Deep Learning.

Plant, cell & environment
Tree-ring data are pivotal for decoding the age and growth patterns of trees, reflecting the impact of environmental factors over time. Addressing the significant shortcomings of traditional, labour-intensive and resource-demanding methods, we propos...

Moving sampling physics-informed neural networks induced by moving mesh PDE.

Neural networks : the official journal of the International Neural Network Society
In this work, we propose an end-to-end adaptive sampling framework based on deep neural networks and the moving mesh method (MMPDE-Net), which can adaptively generate new sampling points by solving the moving mesh PDE. This model focuses on improving...

MYC Rearrangement Prediction From LYSA Whole Slide Images in Large B-Cell Lymphoma: A Multicentric Validation of Self-supervised Deep Learning Models.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Large B-cell lymphoma (LBCL) is a heterogeneous lymphoid malignancy in which MYC gene rearrangement (MYC-R) is associated with a poor prognosis, prompting the recommendation for more intensive treatment. MYC-R detection relies on fluorescence in situ...

Bilinear Perceptual Fusion Algorithm Based on Brain Functional and Structural Data for ASD Diagnosis and Regions of Interest Identification.

Interdisciplinary sciences, computational life sciences
Autism spectrum disorder (ASD) is a serious mental disorder with a complex pathogenesis mechanism and variable presentation among individuals. Although many deep learning algorithms have been used to diagnose ASD, most of them focus on a single modal...