Oncology/Hematology

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

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Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge.

Advancements in sequencing have led to the proliferation of multi-omic profiles of human cells under...

The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning.

Gliomas are primary malignant brain tumors. Monocytes have been proved to actively participate in tu...

Predicting breast cancer 5-year survival using machine learning: A systematic review.

BACKGROUND: Accurately predicting the survival rate of breast cancer patients is a major issue for c...

Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation.

The two-dimensional nature of mammography makes estimation of the overall breast density challenging...

Triage-driven diagnosis of Barrett's esophagus for early detection of esophageal adenocarcinoma using deep learning.

Deep learning methods have been shown to achieve excellent performance on diagnostic tasks, but how ...

Use of Endoscopic Images in the Prediction of Submucosal Invasion of Gastric Neoplasms: Automated Deep Learning Model Development and Usability Study.

BACKGROUND: In a previous study, we examined the use of deep learning models to classify the invasio...

Analysis of Tumor Microenvironment Characteristics in Bladder Cancer: Implications for Immune Checkpoint Inhibitor Therapy.

The tumor microenvironment (TME) plays a crucial role in cancer progression and recent evidence has ...

Applications of Machine and Deep Learning in Adaptive Immunity.

Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and div...

A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images.

The differentiation between major histological types of lung cancer, such as adenocarcinoma (ADC), s...

Latent Correlation Representation Learning for Brain Tumor Segmentation With Missing MRI Modalities.

Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess brain tumor. Accuratel...

Jaya Ant lion optimization-driven Deep recurrent neural network for cancer classification using gene expression data.

Cancer is one of the deadly diseases prevailing worldwide and the patients with cancer are rescued o...

A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks.

Breast cancer is currently the second most common cause of cancer-related death in women. Presently,...

Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks.

BACKGROUND: In oncology, the correct determination of nodal metastatic disease is essential for pati...

A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning.

Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces...

Tens of images can suffice to train neural networks for malignant leukocyte detection.

Convolutional neural networks (CNNs) excel as powerful tools for biomedical image classification. It...

Magnetic tri-bead microrobot assisted near-infrared triggered combined photothermal and chemotherapy of cancer cells.

Magnetic micro/nanorobots attracted much attention in biomedical fields because of their precise mov...

Impact of image compression on deep learning-based mammogram classification.

Image compression is used in several clinical organizations to help address the overhead associated ...

A cell-to-patient machine learning transfer approach uncovers novel basal-like breast cancer prognostic markers amongst alternative splice variants.

BACKGROUND: Breast cancer is amongst the 10 first causes of death in women worldwide. Around 20% of ...

Estimation of tumor parameters using neural networks for inverse bioheat problem.

BACKGROUND AND OBJECTIVE: Some types of cancer cause rapid cell growth, while others cause cells to ...

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