Pathology

Deep Learning-Based Diagnosis of Fatal Hypothermia Using Post-Mortem Computed Tomography.

In forensic medicine, fatal hypothermia diagnosis is not always easy because findings are not specif...

Predicting 3D soft tissue dynamics from 2D imaging using physics informed neural networks.

Tissue dynamics play critical roles in many physiological functions and provide important metrics fo...

Accurate deep learning model using semi-supervised learning and Noisy Student for cervical cancer screening in low magnification images.

Deep learning technology has been used in the medical field to produce devices for clinical practice...

Quantitative analysis of protein dynamics using a deep learning technique combined with experimental cryo-EM density data and MD simulations.

Protein functions associated with biological activity are precisely regulated by both tertiary struc...

Enhancing Total Optical Throughput of Microscopy with Deep Learning for Intravital Observation.

The significance of performing large-depth dynamic microscopic imaging in vivo for life science rese...

Automated large volume sample preparation for vEM.

New developments in electron microscopy technology, improved efficiency of detectors, and artificial...

Optimising trajectory planning for stereotactic brain tumour biopsy using artificial intelligence: a systematic review of the literature.

PURPOSE: Despite advances in technology, stereotactic brain tumour biopsy remains challenging due to...

Histopathological auxiliary system for brain tumour (HAS-Bt) based on weakly supervised learning using a WHO CNS5-style pipeline.

PURPOSE: Classification and grading of central nervous system (CNS) tumours play a critical role in ...

Comparison of surgical completeness in patients operated on conventional open total thyroidectomy (OT) or trans-axillary robot-assisted total thyroidectomy (RATT) by a single axillary approach.

Trans-axillary robot-assisted total thyroidectomy (RATT) is nowadays worldwide accepted but the comp...

Robotic Frykman-Goldberg procedure with natural orifice specimen extraction: A video vignette.

Benign vs malignant vertebral compression fractures with MRI: a comparison between automatic deep learning network and radiologist's assessment.

OBJECTIVE: To test the diagnostic performance of a deep-learning Two-Stream Compare and Contrast Net...

Deep learning-based multi-model approach on electron microscopy image of renal biopsy classification.

BACKGROUND: Electron microscopy is important in the diagnosis of renal disease. For immune-mediated ...

An Investigation about Modern Deep Learning Strategies for Colon Carcinoma Grading.

Developing computer-aided approaches for cancer diagnosis and grading is currently receiving an incr...

Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review.

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality g...

Deep learning for diagnosis of malign pleural effusion on computed tomography images.

BACKGROUND: The pleura is a serous membrane that surrounds the lungs. The visceral surface secretes ...

Global deep learning optimization of chemical exchange saturation transfer magnetic resonance fingerprinting acquisition schedule.

Chemical exchange saturation transfer (CEST) MRI is a promising molecular imaging technique but suff...

Outcome-Supervised Deep Learning on Pathologic Whole Slide Images for Survival Prediction of Immunotherapy in Patients with Non-Small Cell Lung Cancer.

Although programmed death-(ligand) 1 (PD-(L)1) inhibitors are marked by durable efficacy in patients...

Deep learning detection of melanoma metastases in lymph nodes.

BACKGROUND: In melanoma patients, surgical excision of the first draining lymph node, the sentinel l...

PathologyBERT - Pre-trained Vs. A New Transformer Language Model for Pathology Domain.

Pathology text mining is a challenging task given the reporting variability and constant new finding...

From modern CNNs to vision transformers: Assessing the performance, robustness, and classification strategies of deep learning models in histopathology.

While machine learning is currently transforming the field of histopathology, the domain lacks a com...

Deep Learning of Time-Signal Intensity Curves from Dynamic Susceptibility Contrast Imaging Enables Tissue Labeling and Prediction of Survival in Glioblastoma.

BACKGROUND AND PURPOSE: An autoencoder can learn representative time-signal intensity patterns to pr...