Oncology/Hematology

Latest AI and machine learning research in oncology/hematology for healthcare professionals.

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Advanced pathological subtype classification of thyroid cancer using efficientNetB0.

BACKGROUND: Thyroid cancer is a prevalent malignancy requiring accurate subtype identification for e...

Prediction of STAS in lung adenocarcinoma with nodules ≤ 2 cm using machine learning: a multicenter retrospective study.

BACKGROUND AND OBJECTIVE: Spread through air spaces (STAS) is an important factor in determining the...

Continuous nursing symptom management in cancer chemotherapy patients using deep learning.

To assess the efficacy of a deep learning platform for managing symptoms in chemotherapy patients, a...

Development of a MVI associated HCC prognostic model through single cell transcriptomic analysis and 101 machine learning algorithms.

Hepatocellular carcinoma (HCC) is an exceedingly aggressive form of cancer that often carries a poor...

A deep-learning model for quantifying circulating tumour DNA from the density distribution of DNA-fragment lengths.

The quantification of circulating tumour DNA (ctDNA) in blood enables non-invasive surveillance of c...

Hallmarks of artificial intelligence contributions to precision oncology.

The integration of artificial intelligence (AI) into oncology promises to revolutionize cancer care....

Interstitial-guided automatic clinical tumor volume segmentation network for cervical cancer brachytherapy.

Automatic clinical tumor volume (CTV) delineation is pivotal to improving outcomes for interstitial ...

Advanced NLP-driven predictive modeling for tailored treatment strategies in gastrointestinal cancer.

Gastrointestinal cancer represents a significant health burden, necessitating innovative approaches ...

AI integrations with lung cancer screening: Considerations in developing AI in a public health setting.

Lung cancer screening implementation has led to expanded imaging of the chest in older, tobacco-expo...

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 ...

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 ...

Leveraging Deep Learning in Real-Time Intelligent Bladder Tumor Detection During Cystoscopy: A Diagnostic Study.

BACKGROUND: Accurate detection of bladder lesions during cystoscopy is crucial for early tumor diagn...

Preoperative multiclass classification of thymic mass lesions based on radiomics and machine learning.

BACKGROUND: Apart from rare cases such as lymphomas, germ cell tumors, neuroendocrine neoplasms, and...

Automatic detecting multiple bone metastases in breast cancer using deep learning based on low-resolution bone scan images.

Whole-body bone scan (WBS) is usually used as the effective diagnostic method for early-stage and co...

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 occur...

Artificial intelligence for breast cancer screening in mammography (AI-STREAM): preliminary analysis of a prospective multicenter cohort study.

Artificial intelligence (AI) improves the accuracy of mammography screening, but prospective evidenc...

GRATCR: Epitope-Specific T Cell Receptor Sequence Generation With Data-Efficient Pre-Trained Models.

T cell receptors (TCRs) play a crucial role in numerous immunotherapies targeting tumor cells. Howev...

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