Hematology

Lymphoma

Latest AI and machine learning research in lymphoma for healthcare professionals.

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Integrating large language models in systematic reviews: a framework and case study using ROBINS-I for risk of bias assessment.

Large language models (LLMs) may facilitate and expedite systematic reviews, although the approach t...

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 l...

Assessment of non-fatal injuries among university students in Hainan: a machine learning approach to exploring key factors.

BACKGROUND: Injuries constitute a significant global public health concern, particularly among indiv...

DDKG: A Dual Domain Knowledge Guidance strategy for localization and diagnosis of non-displaced femoral neck fractures.

X-ray is the primary tool for diagnosing fractures, crucial for determining their type, location, an...

A novel prediction model for the prognosis of non-small cell lung cancer with clinical routine laboratory indicators: a machine learning approach.

BACKGROUND: Lung cancer is characterized by high morbidity and mortality due to the lack of practica...

Explainable machine learning versus known nomogram for predicting non-sentinel lymph node metastases in breast cancer patients: A comparative study.

INTRODUCTION: Axillary lymph node dissection (ALND) is the standard of care for breast cancer patien...

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects.

Understanding how genetic variants affect the epigenome is key to interpreting GWAS, yet profiling t...

A non-invasive heart rate prediction method using a convolutional approach.

The research focuses on leveraging convolutional neural networks (CNNs) to enhance the analysis of p...

Non-invasive multiple cancer screening using trained detection canines and artificial intelligence: a prospective double-blind study.

The specificity and sensitivity of a simple non-invasive multi-cancer screening method in detecting ...

Mormyroidea-inspired electronic skin for active non-contact three-dimensional tracking and sensing.

The capacity to discern and locate positions in three-dimensional space is crucial for human-machine...

Lymph Node Metastasis Prediction From In Situ Lung Squamous Cell Carcinoma Histopathology Images Using Deep Learning.

Lung squamous cell carcinoma (LUSC), a subtype of non-small cell lung cancer, represents a significa...

From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.

With the increasing demand for precision medicine in cancer patients, radiogenomics emerges as a pro...

MRI denoising with a non-blind deep complex-valued convolutional neural network.

MR images with high signal-to-noise ratio (SNR) provide more diagnostic information. Various methods...

Integrating radiomic and 3D autoencoder-based features for Non-Small Cell Lung Cancer survival analysis.

BACKGROUND AND OBJECTIVES: The aim of this study is to develop a radiomic and deep learning-based si...

A deep learning model for prediction of autism status using whole-exome sequencing data.

Autism is a developmental disability. Research demonstrated that children with autism benefit from e...

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