AIMC Topic: Deep Learning

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Deep Learning-Based 3D Single-Cell Imaging Analysis Pipeline Enables Quantification of Cell-Cell Interaction Dynamics in the Tumor Microenvironment.

Cancer research
UNLABELLED: The three-dimensional (3D) tumor microenvironment (TME) comprises multiple interacting cell types that critically impact tumor pathology and therapeutic response. Efficient 3D imaging assays and analysis tools could facilitate profiling a...

Deep Learning of Multimodal Ultrasound: Stratifying the Response to Neoadjuvant Chemotherapy in Breast Cancer Before Treatment.

The oncologist
BACKGROUND: Not only should resistance to neoadjuvant chemotherapy (NAC) be considered in patients with breast cancer but also the possibility of achieving a pathologic complete response (PCR) after NAC. Our study aims to develop 2 multimodal ultraso...

Integrating pharmacophore model and deep learning for activity prediction of molecules with BRCA1 gene.

Journal of bioinformatics and computational biology
In this paper, we propose a novel approach for predicting the activity/inactivity of molecules with the BRCA1 gene by combining pharmacophore modeling and deep learning techniques. Initially, we generated 3D pharmacophore fingerprints using a pharmac...

Unveiling new patterns: A surgical deep learning model for intestinal obstruction management.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Swift and accurate decision-making is pivotal in managing intestinal obstructions. This study aims to integrate deep learning and surgical expertise to enhance decision-making in intestinal obstruction cases.

An automatic parathyroid recognition and segmentation model based on deep learning of near-infrared autofluorescence imaging.

Cancer medicine
INTRODUCTION: Near-infrared autofluorescence imaging (NIFI) can be used to identify parathyroid gland (PG) during surgery. The purpose of the study is to establish a new model, help surgeons better identify, and protect PGs.

Deep Learning and Machine Learning Algorithms for Retinal Image Analysis in Neurodegenerative Disease: Systematic Review of Datasets and Models.

Translational vision science & technology
PURPOSE: Retinal images contain rich biomarker information for neurodegenerative disease. Recently, deep learning models have been used for automated neurodegenerative disease diagnosis and risk prediction using retinal images with good results.

Ultrafast Brain MRI with Deep Learning Reconstruction for Suspected Acute Ischemic Stroke.

Radiology
Background Deep learning (DL)-accelerated MRI can substantially reduce examination times. However, studies prospectively evaluating the diagnostic performance of DL-accelerated MRI reconstructions in acute suspected stroke are lacking. Purpose To inv...

Geriatric depression and anxiety screening via deep learning using activity tracking and sleep data.

International journal of geriatric psychiatry
BACKGROUND: Geriatric depression and anxiety have been identified as mood disorders commonly associated with the onset of dementia. Currently, the diagnosis of geriatric depression and anxiety relies on self-reported assessments for primary screening...