Pathology

Deep learning enables fast and accurate quantification of MRI-guided near-infrared spectral tomography for breast cancer diagnosis.

The utilization of magnetic resonance (MR) im-aging to guide near-infrared spectral tomography (NIRST) shows significant potential for improving the specificity and sensitivity of breast cancer diagnosis. However, the ef-ficiency and accuracy of NIRST image reconstruction have been limited by the complexities of light propagation mod-eling and MRI image segmentation. To address these chal-lenges, ...

Predicting NSCLC surgical outcomes using deep learning on histopathological images: development and multi-omics validation of Sr-PPS model.

BACKGROUND: Currently, there remains a critical need for reliable tools to accurately predict post-surgical outcomes in non-small cell lung cancer (NSCLC) patients in clinical practice. We aimed to develop and validate a deep learning-based model utilizing histopathological slides to accurately predict post-surgical outcomes in NSCLC patients.

Recent Advances in Applications of Machine Learning in Cervical Cancer Research: A Focus on Prediction Models.

Artificial intelligence (AI) and machine learning (ML) are transforming cervical cancer research and offering advancements in diagnosis, prognosis, sc...

An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution.

High-quality whole-slide scanning is expensive, complex, and time-consuming, thus limiting the acquisition and utilization of high-resolution histopat...

A visual-omics foundation model to bridge histopathology with spatial transcriptomics.

Artificial intelligence has revolutionized computational biology. Recent developments in omics technologies, including single-cell RNA sequencing and ...

Ultrasound image-based contrastive fusion non-invasive liver fibrosis staging algorithm.

OBJECTIVE: The diagnosis of liver fibrosis is usually based on histopathological examination of liver puncture specimens. Although liver puncture is a...

CT-Based Radiomics for Predicting PD-L1 Expression in Non-small Cell Lung Cancer: A Systematic Review and Meta-analysis.

BACKGROUND AND PURPOSE: The efficacy of immunotherapy in non-small cell lung cancer (NSCLC) is intricately associated with baseline PD-L1 expression r...

A generalist deep-learning volume segmentation tool for volume electron microscopy of biological samples.

We present the Volume Segmentation Tool (VST), a deep learning software tool that implements volumetric image segmentation in volume electron microsco...

Rapid differentiation of patients with lung cancers from benign lung nodule based on dried serum Fourier-transform infrared spectroscopy combined with machine learning algorithms.

Lung cancer (LC) is associated with poor 5-year survival rates when diagnosed at advanced stages. While low-dose computed tomography (LDCT) screening ...

Ensemble learning approach for detecting breast invasive ductal carcinoma from histopathological images.

Invasive ductal carcinoma is a type of breast cancer that is one of the most frequent and aggressive forms of breast malignancy, necessitating accurat...

Deep Learning-Assisted Microscopic Polarization Inspection of Micro-Nano Damage Precursors: Automatic, Non-Destructive Metrology for Additive Manufacturing Devices.

Additive Manufacturing (AM), as a revolutionary breakthrough in advanced manufacturing paradigms, leverages its unique layer-by-layer construction adv...

Optimizing Artificial Intelligence Thresholds for Mammographic Lesion Detection: A Retrospective Study on Diagnostic Performance and Radiologist-Artificial Intelligence Discordance.

Artificial intelligence (AI)-based systems are increasingly being used to assist radiologists in detecting breast cancer on mammograms. However, appl...

Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care.

Musculoskeletal tumors present a diagnostic challenge due to their rarity, histological diversity, and overlapping imaging features. Accurate characte...

Navigating the future of fertility preservation: advanced predictive strategies for treatment outcomes of endometrial atypical hyperplasia and carcinoma.

Due to the decreasing age of onset and the postponement of childbearing, there is a growing number of patients with endometrial carcinoma (EC) and end...

Pathomics in Gastrointestinal Tumors: Research Progress and Clinical Applications.

Gastrointestinal tumors are among the malignancies with the highest global incidence and mortality rates, and their diagnosis and treatment heavily re...

Improving cancer detection through computer-aided diagnosis: A comprehensive analysis of nonlinear and texture features in breast thermograms.

Breast cancer is a significant health issue for women, characterized by its high rates of mortality and sickness. However, its early detection is cruc...

Hyperspectral imaging to characterize the vegetative tissue biochemical changes in response to water deficit conditions in sorghum ().

Hyperspectral imaging has been used to determine plant stress status. However, the biological interpretation of the spectral changes remain less explo...

Multimodal Machine Learning Analysis of GaSe Molecular Beam Epitaxy Growth Conditions.

Autonomous synthesis platforms integrating machine learning with in situ diagnostics have the potential to revolutionize thin-film growth by enabling ...

Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical features.

BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to ipsilateral invasive breast cancer (IBC) but over 75% of DCIS lesions do not progress if u...

Near-Freezing-Temperature Golgi Neuronal Staining for X-ray Imaging of Human Brain.

Achieving detailed neuronal structural information in large-volume brain tissue has been a longstanding challenge in human brain imaging. A key obstac...