Oncology/Hematology

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

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KanCell: dissecting cellular heterogeneity in biological tissues through integrated single-cell and spatial transcriptomics.

KanCell is a deep learning model based on Kolmogorov-Arnold networks (KAN) designed to enhance cellu...

Breast radiation therapy fluence painting with multi-agent deep reinforcement learning.

BACKGROUND: The electronic compensation (ECOMP) technique for breast radiation therapy provides exce...

Deep learning model targeting cancer surrounding tissues for accurate cancer diagnosis based on histopathological images.

Accurate and fast histological diagnosis of cancers is crucial for successful treatment. The deep le...

A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images.

Neoadjuvant chemotherapy (NAC) is a systemic and systematic chemotherapy regimen for breast cancer p...

Classifying tumour infiltrating lymphocytes in oral squamous cell carcinoma histopathology using joint learning framework.

Oral squamous cell carcinoma (OSCC) is the most common form of oral cancer, with increasing global i...

Identification of potential biomarkers for 2022 Mpox virus infection: a transcriptomic network analysis and machine learning approach.

Monkeypox virus (MPXV), a zoonotic pathogen, re-emerged in 2022 with the Clade IIb variant, raising ...

Deep Learning Enabled Scoring of Pancreatic Neuroendocrine Tumors Based on Cancer Infiltration Patterns.

Pancreatic neuroendocrine tumors (PanNETs) are a heterogeneous group of neoplasms that include tumor...

The tumour histopathology "glossary" for AI developers.

The applications of artificial intelligence (AI) and deep learning (DL) are leading to significant a...

Colorimetric aptasensor coupled with a deep-learning-powered smartphone app for programmed death ligand-1 expressing extracellular vesicles.

Lung cancer is a devastating public health threat and a leading cause of cancer-related deaths. Ther...

Feature selection enhances peptide binding predictions for TCR-specific interactions.

INTRODUCTION: T-cell receptors (TCRs) play a critical role in the immune response by recognizing spe...

Optimizing kinase and PARP inhibitor combinations through machine learning and in silico approaches for targeted brain cancer therapy.

The drug combination is an attractive approach for cancer treatment. PARP and kinase inhibitors have...

CTCNet: a fine-grained classification network for fluorescence images of circulating tumor cells.

The identification and categorization of circulating tumor cells (CTCs) in peripheral blood are impe...

Classifying the molecular subtype of breast cancer using vision transformer and convolutional neural network features.

PURPOSE: Identification of the molecular subtypes in breast cancer allows to optimize treatment stra...

Predicting Postoperative Infection After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy with Splenectomy.

BACKGROUND: Hematologic changes after splenectomy and hyperthermic intraperitoneal chemotherapy (HIP...

Multimodal integration using a machine learning approach facilitates risk stratification in HR+/HER2- breast cancer.

Hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast can...

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