Deep learning based multimodal biomedical data fusion: an overview and comparative review
10.1016/j.inffus.2024.102536 · doi-reference
Anti-senescent drug screening by deep learning-based morphology senescence scoring
10.1038/s41467-020-20213-0 · doi-reference
Deep learning for drug discovery and cancer research: automated analysis of vascularization images
10.1109/tcbb.2018.2841396 · doi-reference
Development of an AI model for DILI-level prediction using liver organoid brightfield images
10.1038/s42003-025-08205-6 · doi-reference
Intelligent classification of platelet aggregates by agonist type
10.7554/elife.52938 · doi-reference
Intelligent whole-blood imaging flow cytometry for simple, rapid, and cost-effective drug-susceptibility testing of leukemia
10.1039/c8lc01370e · doi-reference
Label-free detection of cellular drug responses by high-throughput bright-field imaging and machine learning
10.1038/s41598-017-12378-4 · doi-reference
Diagnosis of sepsis from a drop of blood by measurement of spontaneous neutrophil motility in a microfluidic assay
10.1038/s41551-018-0208-z · doi-reference
Label-free, high-throughput holographic screening and enumeration of tumor cells in blood
10.1039/c7lc00149e · doi-reference
Automated evaluation of tumor spheroid behaviour in 3D culture using deep learning-based recognition
10.1016/j.biomaterials.2021.120770 · doi-reference
Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin
10.1371/journal.pcbi.1008946 · doi-reference
Sickle cell disease biochip: a functional red blood cell adhesion assay for monitoring sickle cell disease
10.1016/j.trsl.2016.03.008 · doi-reference
Automated deep lineage tree analysis using a bayesian single cell tracking approach
10.3389/fcomp.2021.734559 · doi-reference
Tracking cell lineages in 3D by incremental deep learning
10.7554/elife.69380 · doi-reference
OrganoidTracker: efficient cell tracking using machine learning and manual error correction
10.1371/journal.pone.0240802 · doi-reference
Prospective identification of hematopoietic lineage choice by deep learning
10.1038/nmeth.4182 · doi-reference
AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks
10.1038/s41598-018-22181-4 · doi-reference
Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells
10.1371/journal.pone.0246988 · doi-reference
Automated quantification of immunomagnetic beads and leukemia cells from optical microscope images
10.1016/j.bspc.2019.01.002 · doi-reference
Navigating the landscape of multimodal AI in medicine: a scoping review on technical challenges and clinical applications
10.1038/s41598-024-52007-5 · doi-reference
Microfluidics guided by deep learning for cancer immunotherapy screening
10.1073/pnas.2214569119 · doi-reference
Assessing red blood cell deformability from microscopy images using deep learning
10.1039/d1lc01006a · doi-reference
Deep learning assisted mechanotyping of individual cells through repeated deformations and relaxations in undulating channels
10.1063/5.0077432 · doi-reference
CD4+ versus CD8+ T-lymphocyte identification in an integrated microfluidic chip using light scattering and machine learning
10.1039/c9lc00695h · doi-reference
Automated biophysical classification of apoptotic pancreatic cancer cell subpopulations by using machine learning approaches with impedance cytometry
10.1039/d2lc00304j · doi-reference
Neural network-enhanced real-time impedance flow cytometry for single-cell intrinsic characterization
10.1039/d1lc00755f · doi-reference
Cell cycle stage classification using phase imaging with computational specificity
10.1021/acsphotonics.1c01779 · doi-reference
DeepCycle reconstructs a cyclic cell cycle trajectory from unsegmented cell images using convolutional neural networks
10.15252/msb.20209474 · doi-reference
Reconstructing cell cycle and disease progression using deep learning
10.1038/s41467-017-00623-3 · doi-reference
DetecDiv, a generalist deep-learning platform for automated cell division tracking and survival analysis
10.7554/elife.79519 · doi-reference
Deep learning-enabled virtual histological staining of biological samples
10.1038/s41377-023-01104-7 · doi-reference
Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning
10.1038/s41551-019-0362-y · doi-reference
Usiigaci: instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning
10.1016/j.softx.2019.02.007 · doi-reference
Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments
10.1371/journal.pcbi.1005177 · doi-reference
U-net: convolutional networks for biomedical image segmentation
10.1007/978-3-319-24574-4_28 · doi-reference
An integrated microreactor system for self-optimization of a heck reaction: from micro-to mesoscale flow systems
10.1002/anie.201002590 · doi-reference
An automated microfluidic system for online optimization in chemical synthesis
10.1021/op100123e · doi-reference
Online monitoring and analysis for autonomous continuous flow self-optimizing reactor systems
10.1039/c5re00038f · doi-reference
Online detection and automation methods in microfluidic nanomaterial synthesis
10.1016/j.coche.2015.01.007 · doi-reference
Feedback in flow for accelerated reaction development
10.1021/acs.accounts.6b00261 · doi-reference
