Pathology

MultiSCCHisto-Net-KD: A deep network for multi-organ explainable squamous cell carcinoma diagnosis with knowledge distillation.

Squamous cell carcinoma is a prevalent cancer type that affects various organs in the human body. Manual analysis for detecting squamous cell carcinoma in histopathological images is time-consuming and may be subjective. Squamous cell carcinoma diagnosis is typically based on the differences in the architectural arrangement of squamous epithelial layers and the presence of keratinization. However,...

Let it shine: Autofluorescence of Papanicolaou-stain improves AI-based cytological oral cancer detection.

BACKGROUND AND OBJECTIVES: Oral cancer is a global health challenge. The disease can be successfully treated if detected early, but the survival rate drops significantly for late stage cases. There is a growing interest in a shift from the current standard of invasive and time-consuming tissue sampling and histological examination, towards non-invasive brush biopsies and cytological examination, f...

Diagnosis of infections using artificial intelligence techniques versus standard microscopy in a reference laboratory.

Diagnosing malaria using standard techniques is time-consuming. With limited staffing in many laboratories, this may lead to delays in reporting. Inno...

Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids.

UNLABELLED: Bacterial vaginosis (BV) is an abnormal gynecological condition caused by the overgrowth of specific bacteria in the vagina. This study ai...

MoMA: Momentum contrastive learning with multi-head attention-based knowledge distillation for histopathology image analysis.

There is no doubt that advanced artificial intelligence models and high quality data are the keys to success in developing computational pathology too...

A programmable, open-source robot that scratches cultured tissues to investigate cell migration, healing, and tissue sculpting.

Despite the widespread popularity of the "scratch assay," where a pipette is dragged manually through cultured tissue to create a gap to study cell mi...

High-throughput platform for label-free sorting of 3D spheroids using deep learning.

End-stage liver diseases have an increasing impact worldwide, exacerbated by the shortage of transplantable organs. Recognized as one of the promising...

Neurodegeneration: 2024 update.

This review highlights a collection of both diverse and highly impactful studies published in the previous year selected by the author from the neurod...

Integration of AI-Assisted in Digital Cervical Cytology Training: A Comparative Study.

OBJECTIVE: This study aimed to investigate the supporting role of artificial intelligence (AI) in digital cervical cytology training.

Fatal fall from a height: is it possible to apply artificial intelligence techniques for height estimation?

Fall from a height trauma is characterized by a multiplicity of injuries, related to multiple factors. The height of the fall is the factor that most ...

AI-driven feature selection and epigenetic pattern analysis: A screening strategy of CpGs validated by pyrosequencing for body fluid identification.

Identification of body fluid stain at crime scene is one of the important tasks of forensic evidence analysis. Currently, body fluid-specific CpGs det...

Deep learning using histological images for gene mutation prediction in lung cancer: a multicentre retrospective study.

BACKGROUND: Accurate detection of driver gene mutations is crucial for treatment planning and predicting prognosis for patients with lung cancer. Conv...

Applications of artificial intelligence in regenerative dentistry: promoting stem cell therapy and the scaffold development.

Tissue repair represents a critical concern within the domain of dentistry. On a daily basis, countless individuals seek dental clinic services due to...

Diagnostic modalities in the mediastinum and the role of bronchoscopy in mediastinal assessment: a narrative review.

BACKGROUND AND OBJECTIVE: Diagnosis of pathology in the mediastinum has proven quite challenging, given the wide variability of both benign and malign...

Automatic segmentation of pericardial adipose tissue from cardiac MR images via semi-supervised method with difference-guided consistency.

BACKGROUND: Accurate and automatic segmentation of pericardial adipose tissue (PEAT) in cardiac magnetic resonance (MR) images is essential for the di...

Object-based feedback attention in convolutional neural networks improves tumour detection in digital pathology.

Human visual attention allows prior knowledge or expectations to influence visual processing, allocating limited computational resources to only that ...

Transfer Contrastive Learning for Raman Spectroscopy Skin Cancer Tissue Classification.

Using Raman spectroscopy (RS) signals for skin cancer tissue classification has recently drawn significant attention, because of its non-invasive opti...

A multimodal machine learning model for the stratification of breast cancer risk.

Machine learning models for the diagnosis of breast cancer can facilitate the prediction of cancer risk and subsequent patient management among other ...

Deep learning-driven multi-omics sequential diagnosis with Hybrid-OmniSeq: Unraveling breast cancer complexity.

BackgroundBreast cancer results from an uncontrolled growth of breast tissue. Many methods of diagnosis are using multi-omics data to better understan...

Machine learning-based analysis of programmed cell death types and key genes in intervertebral disc degeneration.

Intervertebral disc degeneration (IVDD) is intricately associated with various forms of programmed cell death (PCD). Identifying key PCD types and ass...