Pathology

SeeSaw: Learning Soft Tissue Deformation From Laparoscopy Videos With GNNs.

A major challenge in image-guided laparoscopic surgery is that structures of interest often deform and go, even if only momentarily, out of view. Methods which rely on having an up-to-date impression of those structures, such as registration or localisation, are undermined in these circumstances. This is particularly true for soft-tissue structures that continually change shape - in registration, ...

In-context learning enables multimodal large language models to classify cancer pathology images.

Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models. However, this process is computationally and technically demanding. In language processing, in-context learning provides an alternative, where models learn from within prompts, bypassing the need for parameter updates. Yet, in-context lear...

Reconstructing 3D histological structures using machine learning (artificial intelligence) algorithms.

BACKGROUND: Histomorphometry is currently the gold standard for bone microarchitectural examinations. This relies on two-dimensional (2D) sections to ...

The Future of Surgical Diagnostics: Artificial Intelligence-Enhanced Detection of Ganglion Cells for Hirschsprung Disease.

Hirschsprung disease, a congenital disease characterized by the absence of ganglion cells, presents significant surgical challenges. Addressing a crit...

A Multi-task learning U-Net model for end-to-end HEp-2 cell image analysis.

Antinuclear Antibody (ANA) testing is pivotal to help diagnose patients with a suspected autoimmune disease. The Indirect Immunofluorescence (IIF) mic...

A systematic review on feature extraction methods and deep learning models for detection of cancerous lung nodules at an early stage -the recent trends and challenges.

Lung cancer is one of the most common life-threatening worldwide cancers affecting both the male and the female populations. The appearance of nodules...

Prediction of benign and malignant pulmonary nodules using preoperative CT features: using PNI-GARS as a predictor.

PURPOSE: The aim of this study was to develop and validate a prediction model for classification of pulmonary nodules based on preoperative CT imaging...

Machine learning approach in canine mammary tumour classification using rapid evaporative ionization mass spectrometry.

Rapid evaporative ionization mass spectrometry (REIMS) coupled with a monopolar handpiece used for surgical resection and combined with chemometrics h...

Automated Single Cell Phenotyping of Time-of-Flight Secondary Ion Mass Spectrometry Tissue Images.

Existing analytical techniques are being improved or applied in new ways to profile the tissue microenvironment (TME) to better understand the role of...

Towards real-time myocardial infarction diagnosis: a convergence of machine learning and ion-exchange membrane technologies leveraging miRNA signatures.

Rapid diagnosis of acute myocardial infarction (AMI) is crucial for optimal patient management. Accurate diagnosis and time of onset of an acute event...

Experts fail to reliably detect AI-generated histological data.

AI-based methods to generate images have seen unprecedented advances in recent years challenging both image forensic and human perceptual capabilities...

Explainable Machine Learning Models Using Robust Cancer Biomarkers Identification from Paired Differential Gene Expression.

In oncology, there is a critical need for robust biomarkers that can be easily translated into the clinic. We introduce a novel approach using paired ...

Diagnosis and typing of leukemia using a single peripheral blood cell through deep learning.

Leukemia is highly heterogeneous, meaning that different types of leukemia require different treatments and have different prognoses. Current clinical...

Whole Slide Imaging, Artificial Intelligence, and Machine Learning in Pediatric and Perinatal Pathology: Current Status and Future Directions.

The integration of artificial intelligence (AI) into healthcare is becoming increasingly mainstream. Leveraging digital technologies, such as AI and d...

Overcoming the preferred-orientation problem in cryo-EM with self-supervised deep learning.

While advances in single-particle cryo-EM have enabled the structural determination of macromolecular complexes at atomic resolution, particle orienta...

Multi-classification of breast cancer pathology images based on a two-stage hybrid network.

BACKGROUND AND OBJECTIVE: In current clinical medicine, pathological image diagnosis is the gold standard for cancer diagnosis. After pathologists det...

Generating and evaluating synthetic data in digital pathology through diffusion models.

Synthetic data is becoming a valuable tool for computational pathologists, aiding in tasks like data augmentation and addressing data scarcity and pri...

The study on ultrasound image classification using a dual-branch model based on Resnet50 guided by U-net segmentation results.

In recent years, the incidence of nodular thyroid diseases has been increasing annually. Ultrasonography has become a routine diagnostic tool for thyr...

In Vivo Time-Resolved Fluorescence Detection of Liver Cancer Supported by Machine Learning.

OBJECTIVES: One of the widely used optical biopsy methods for monitoring cellular and tissue metabolism is time-resolved fluorescence. The use of this...

Intraoperative label-free tissue diagnostics using a stimulated Raman histology imaging system with artificial intelligence: An initial experience.

BACKGROUND: Accurate intraoperative tissue diagnostics could impact on decision making regarding the extent of resection (EOR) during brain tumor surg...