Pathology

Self-supervised U-transformer network with mask reconstruction for metal artifact reduction.

. Metal artifacts severely damaged human tissue information from the computed tomography (CT) image, posing significant challenges to disease diagnosis. Deep learning has been widely explored for the metal artifact reduction (MAR) task. Nevertheless, paired metal artifact CT datasets suitable for training do not exist in reality. Although the synthetic CT image dataset provides additional training...

Accuracy of deep learning models in the detection of accessory ostium in coronal cone beam computed tomographic images.

Accessory ostium [AO] is one of the important anatomical variations in the maxillary sinus. AO is often associated with sinus pathology. Radiographic imaging plays a very important role in the detection of AO. Deep learning models have been used in maxillofacial imaging for interpretation and segmentation. However, there have been no research papers investigating the effectiveness of CNN in detect...

Harnessing Artificial Intelligence for Precision Diagnosis and Treatment of Triple Negative Breast Cancer.

Triple-Negative Breast Cancer (TNBC) is a highly aggressive subtype of breast cancer (BC) characterized by the absence of estrogen, progesterone, and ...

Diagnostic value of deep learning of multimodal imaging of thyroid for TI-RADS category 3-5 classification.

BACKGROUND: Thyroid nodules classified within the Thyroid Imaging Reporting and Data Systems (TI-RADS) category 3-5 are typically regarded as having v...

Artificial intelligence and whole slide imaging, a new tool for the microsatellite instability prediction in colorectal cancer: Friend or foe?

Colorectal cancer (CRC) is the third most common and second most deadly cancer worldwide. Despite advances in screening and treatment, CRC is heteroge...

Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer.

Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-eosin-stained whole s...

Terahertz molecular vibrational sensing using 3D printed anapole meta-biosensor.

Terahertz (THz) fingerprint sensing utilizes the absorption of fingerprints generated by the unique vibrational characteristics of molecules to achiev...

Interpretable Multimodal Fusion Model for Bridged Histology and Genomics Survival Prediction in Pan-Cancer.

Understanding the prognosis of cancer patients is crucial for enabling precise diagnosis and treatment by clinical practitioners. Multimodal fusion mo...

A comprehensive approach to anticipating the progression of mild cognitive impairment.

The immersive experience provided by our approach empowers researchers with an intuitive exploration of brain structures. Within the brain's central n...

Accurate phenotyping of luminal A breast cancer in magnetic resonance imaging: A new 3D CNN approach.

Breast cancer (BC) remains a predominant and deadly cancer in women worldwide. By 2040, projections indicate that more than 3 million new cases of bre...

A multi-stage fusion deep learning framework merging local patterns with attention-driven contextual dependencies for cancer detection.

Cancer is a severe threat to public health. Early diagnosis of disease is critical, but the lack of experts in this field, the personal assessment pro...

Leveraging swin transformer with ensemble of deep learning model for cervical cancer screening using colposcopy images.

Cervical cancer (CC) is the leading cancer, which mainly affects women worldwide. It generally occurs from abnormal cell evolution in the cervix and a...

Deep Augmented Metric Learning Network for Prostate Cancer Classification in Ultrasound Images.

Prostate cancer screening often relies on cost-intensive MRIs and invasive needle biopsies. Transrectal ultrasound imaging, as a more affordable and n...

A unified approach to medical image segmentation by leveraging mixed supervision and self and transfer learning (MIST).

Medical image segmentation is important for quantitative disease diagnosis and treatment but relies on accurate pixel-wise labels, which are costly, t...

Predicting inflammatory response of biomimetic nanofibre scaffolds for tissue regeneration using machine learning and graph theory.

Tissue regeneration after a wound occurs through three main overlapping and interrelated stages namely inflammatory, proliferative, and remodelling ph...

Cold storage surpasses the impact of biological age and donor characteristics on red blood cell morphology classified by deep machine learning.

Assessment of the morphology of red blood cells (RBCs) can improve clinical benefits following blood transfusion. Deep machine learning surpasses trad...

Spatiotemporal feature learning for actin dynamics.

The social amoeba Dictyostelium discoideum is a standard model system for studying cell motility and formation of biological patterns. D. discoideum c...

Comparison of the accuracy of GPT-4 and resident physicians in differentiating benign and malignant thyroid nodules.

OBJECTIVE: To assess the diagnostic performance of the GPT-4 model in comparison to resident physicians in distinguishing between benign and malignant...

Discovery of mutations predictive of survival benefit from immunotherapy in first-line NSCLC: A retrospective machine learning study of IMpower150 liquid biopsy data.

Predictive biomarker identification in cancer treatment has traditionally relied on pre-defined analyses, limiting discoveries to expected biomarkers ...

Enhancing diagnosis prediction with adaptive disease representation learning.

Diagnosis prediction predicts which diseases a patient is most likely to suffer from in the future based on their historical electronic health records...