Oncology/Hematology

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

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Auto-segmentation of organs at risk for head and neck radiotherapy planning: From atlas-based to deep learning methods.

Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck (H&N), wh...

Machine learning for predicting pathological complete response in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy.

For patients with locally advanced rectal cancer (LARC), achieving a pathological complete response ...

Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images.

BACKGROUND: Multiplex immunohistochemistry (mIHC) permits the labeling of six or more distinct cell ...

Morphogo: An Automatic Bone Marrow Cell Classification System on Digital Images Analyzed by Artificial Intelligence.

INTRODUCTION: The nucleated-cell differential count on the bone marrow aspirate smears is required f...

Correlation Between the Trajectory of the Center of Pressure and Thermography of Cancer Patients Undergoing Chemotherapy.

OBJECTIVE: To correlate the potential of the stabilometric parameters of baropodometry with the supe...

Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging.

Imaging plays a key role in the management of brain tumors, including the diagnosis, prognosis, and ...

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis.

We use deep transfer learning to quantify histopathological patterns across 17,355 hematoxylin and e...

Pan-cancer image-based detection of clinically actionable genetic alterations.

Molecular alterations in cancer can cause phenotypic changes in tumor cells and their micro-environm...

Combination of Estradiol with Leukemia Inhibitory Factor Stimulates Granulosa Cells Differentiation into Oocyte-Like Cells.

Previous studies have documented that cumulus granulosa cells (GCs) can trans-differentiation into ...

A Deep Learning-Based Tumor Classifier Directly Using MS Raw Data.

Since the launch of Chinese Human Proteome Project (CNHPP) and Clinical Proteomic Tumor Analysis Con...

Three-Dimensional Neural Network to Automatically Assess Liver Tumor Burden Change on Consecutive Liver MRIs.

BACKGROUND: Tumor response to therapy is often assessed by measuring change in liver lesion size bet...

Strategies for Testing Intervention Matching Schemes in Cancer.

Personalized medicine, or the tailoring of health interventions to an individual's nuanced and often...

Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics.

Cancer is driven by genomic alterations, but the processes causing this disease are largely performe...

MAGPEL: an autoMated pipeline for inferring vAriant-driven Gene PanEls from the full-length biomedical literature.

In spite of the efforts in developing and maintaining accurate variant databases, a large number of ...

: deep learning-based radiomics for the time-to-event outcome prediction in lung cancer.

Hand-crafted radiomics has been used for developing models in order to predict time-to-event clinica...

Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning-based radiomics.

OBJECTIVE: To investigate the efficacy of contrast-enhanced computed tomography (CECT)-based radiomi...

Development and Validation of a Deep-learning Model to Assist With Renal Cell Carcinoma Histopathologic Interpretation.

OBJECTIVE: To develop and test the ability of a convolutional neural network (CNN) to accurately ide...

Attention-Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images.

Incorporating human domain knowledge for breast tumor diagnosis is challenging because shape, bounda...

Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer.

BACKGROUND: Radiogenomics is an emerging field that integrates "Radiomics" and "Genomics". In the cu...

The Impact of Artificial Intelligence in the Endoscopic Assessment of Premalignant and Malignant Esophageal Lesions: Present and Future.

In the gastroenterology field, the impact of artificial intelligence was investigated for the purpos...

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