Pathology

Weakly Supervised Histopathology Image Segmentation With Sparse Point Annotations.

Digital histopathology image segmentation can facilitate computer-assisted cancer diagnostics. Given the difficulty of obtaining manual annotations, weak supervision is more suitable for the task than full supervision is. However, most weakly supervised models are not ideal for handling severe intra-class heterogeneity and inter-class homogeneity in histopathology images. Therefore, we propose a n...

Generalized Fixation Invariant Nuclei Detection Through Domain Adaptation Based Deep Learning.

Nucleus detection is a fundamental task in histological image analysis and an important tool for many follow up analyses. It is known that sample preparation and scanning procedure of histological slides introduce a great amount of variability to the histological images and poses challenges for automated nucleus detection. Here, we studied the effect of histopathological sample fixation on the acc...

A Visually Interpretable Deep Learning Framework for Histopathological Image-Based Skin Cancer Diagnosis.

Owing to the high incidence rate and the severe impact of skin cancer, the precise diagnosis of malignant skin tumors is a significant goal, especiall...

A method for utilizing automated machine learning for histopathological classification of testis based on Johnsen scores.

We examined whether a tool for determining Johnsen scores automatically using artificial intelligence (AI) could be used in place of traditional Johns...

High-resolution single-shot phase-shifting interference microscopy using deep neural network for quantitative phase imaging of biological samples.

White light phase-shifting interference microscopy (WL-PSIM) is a prominent technique for high-resolution quantitative phase imaging (QPI) of industri...

Extraperitoneal Laparoscopic Versus Transperitoneal Robot-Assisted Laparoscopic Approaches for Extended Pelvic Lymph Node Dissection During Radical Prostatectomy.

We aim to directly compare the feasibility and safety of extended pelvic lymph node dissection (PLND) during transperitoneal robotic-assisted radical...

Digital Frozen Sections with Fluorescence Confocal Microscopy During Robot-assisted Radical Prostatectomy: Surgical Technique.

BACKGROUND: Robot-assisted radical prostatectomy (RARP) involves a tradeoff between oncological control and functional outcomes. Intraoperative contro...

A Zwitterionic-Aromatic Motif-Based ionic skin for highly biocompatible and Glucose-Responsive sensor.

Electronic skins that can sense external stimuli have been of great significance in artificial intelligence and smart wearable devices in recent years...

Development of a transperineal prostate biopsy robot guided by MRI-TRUS image.

BACKGROUND: In the transrectal ultrasound (TRUS)-guided transperineal prostate biopsy, doctors determine the biopsy target by observing the prostate r...

Artificial intelligence assists identifying malignant versus benign liver lesions using contrast-enhanced ultrasound.

BACKGROUND AND AIM: This study aims to construct a strategy that uses assistance from artificial intelligence (AI) to assist radiologists in the ident...

Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer.

OBJECTIVE: To develop a new digital biomarker based on the analysis of primary tumour tissue by a convolutional neural network (CNN) to predict lymph ...

Clinical use of machine learning-based pathomics signature for diagnosis and survival prediction of bladder cancer.

Traditional histopathology performed by pathologists by the naked eye is insufficient for accurate and efficient diagnosis of bladder cancer (BCa). We...

AI-based pathology predicts origins for cancers of unknown primary.

Cancer of unknown primary (CUP) origin is an enigmatic group of diagnoses in which the primary anatomical site of tumour origin cannot be determined. ...

Impact of MRI/US fusion-guided prostate biopsy on biopsy-naïve patients: A single urologist's experience.

OBJECTIVES: To report our experience with imaging-guided targeted prostate biopsy (IGTpBx) for patients undergoing initial prostate biopsy in a clinic...

Neural network strategies for plasma membrane selection in fluorescence microscopy images.

In recent years, there has been an explosion of fluorescence microscopy studies of live cells in the literature. The analysis of the images obtained i...

Machine learning applied to near-infrared spectra for clinical pleural effusion classification.

Lung cancer patients with malignant pleural effusions (MPE) have a particular poor prognosis. It is crucial to distinguish MPE from benign pleural eff...

Development and Validation of a Deep Learning Model to Quantify Interstitial Fibrosis and Tubular Atrophy From Kidney Ultrasonography Images.

IMPORTANCE: Interstitial fibrosis and tubular atrophy (IFTA) is a strong indicator of decline in kidney function and is measured using histopathologic...

Automated assessment of glomerulosclerosis and tubular atrophy using deep learning.

In kidney transplantations, pathologists evaluate the architecture of both glomeruli, interstitium and tubules to assess the nephron status. An accura...

A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy.

Ultrasound Localization Microscopy (ULM) can resolve the microvascular bed down to a few micrometers. To achieve such performance, microbubble contras...

Synthetic Database of Aortic Morphometry and Hemodynamics: Overcoming Medical Imaging Data Availability.

Modeling of hemodynamics and artificial intelligence have great potential to support clinical diagnosis and decision making. While hemodynamics modeli...