Oncology/Hematology

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

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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...

Syn-Net: A Synchronous Frequency-Perception Fusion Network for Breast Tumor Segmentation in Ultrasound Images.

Accurate breast tumor segmentation in ultrasound images is a crucial step in medical diagnosis and l...

CDAF-Net: A Contextual Contrast Detail Attention Feature Fusion Network for Low-Dose CT Denoising.

Low-dose computed tomography (LDCT) is a specialized CT scan with a lower radiation dose than normal...

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. Transrec...

Enhancing Drug Repositioning Through Local Interactive Learning With Bilinear Attention Networks.

Drug repositioning has emerged as a promising strategy for identifying new therapeutic applications ...

TARSL: Triple-Attention Cross-Network Representation Learning to Predict Synthetic Lethality for Anti-Cancer Drug Discovery.

Cancer is a multifaceted disease that results from co-mutations of multi biological molecules. A pro...

StackTHP: A stacking ensemble model for accurate prediction of tumor-homing peptides in cancer therapy.

The tumor-homing peptides (THPs) have emerged as one of the attractive resources for targeted cancer...

Deep learning for hepatocellular carcinoma recurrence before and after liver transplantation: a multicenter cohort study.

Hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) is a major contributor to...

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