Oncology/Hematology

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

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Deep learning-based virtual noncalcium imaging in multiple myeloma using dual-energy CT.

BACKGROUND: Dual-energy CT with virtual noncalcium (VNCa) images allows the evaluation of focal intr...

Deep learning captures selective features for discrimination of microsatellite instability from pathologic tissue slides of gastric cancer.

Microsatellite instability (MSI) status is an important prognostic marker for various cancers. Furth...

Multimodal deep learning model on interim [F]FDG PET/CT for predicting primary treatment failure in diffuse large B-cell lymphoma.

OBJECTIVES: The prediction of primary treatment failure (PTF) is necessary for patients with diffuse...

A data augmentation method for fully automatic brain tumor segmentation.

Automatic segmentation of glioma and its subregions is of great significance for diagnosis, treatmen...

Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment.

Nowadays, artificial intelligence (AI) creates numerous promising opportunities in the life sciences...

Research on imbalance machine learning methods for MRWI soft tissue sarcoma data.

BACKGROUND: Soft tissue sarcoma is a rare and highly heterogeneous tumor in clinical practice. Patho...

EpICC: A Bayesian neural network model with uncertainty correction for a more accurate classification of cancer.

Accurate classification of cancers into their types and subtypes holds the key for choosing the righ...

Deep learning method for reducing metal artifacts in dental cone-beam CT using supplementary information from intra-oral scan.

Recently, dental cone-beam computed tomography (CBCT) methods have been improved to significantly re...

Clinical and Biological Significances of a Ferroptosis-Related Gene Signature in Lung Cancer Based on Deep Learning.

Acyl-CoA synthetase long-chain family member 4 (ACSL4) has been linked to the occurrence of tumors a...

Utility of Fully Automated Body Composition Measures on Pretreatment Abdominal CT for Predicting Survival in Patients With Colorectal Cancer.

CT examinations contain opportunistic body composition data with potential prognostic utility. Prev...

Tumor Microenvironment, Radiology, and Artificial Intelligence: Should We Consider Tumor Periphery?

OBJECTIVES: The tumor microenvironment (TME) consists of cellular and noncellular components which e...

Morphologic Features and Deep Learning-Based Analysis of Canine Spermatogenic Stages.

In nonclinical toxicity studies, stage-aware evaluation is often expected to assess drug-induced tes...

Automated Detection and Characterization of Colon Cancer with Deep Convolutional Neural Networks.

Colon cancer is a momentous reason for illness and death in people. The conclusive diagnosis of colo...

A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural Networks.

Recent improvements in current technology have had a significant impact on a wide range of image pro...

MSFR-Net: Multi-modality and single-modality feature recalibration network for brain tumor segmentation.

BACKGROUND: Accurate and automated brain tumor segmentation from multi-modality MR images plays a si...

Therapy resistance mechanisms in hematological malignancies.

Hematologic malignancies are model diseases for understanding neoplastic transformation and serve as...

Deep Learning-based Detection of Solid and Cystic Pancreatic Neoplasms at Contrast-enhanced CT.

Background Deep learning (DL) may facilitate the diagnosis of various pancreatic lesions at imaging....

Artificial intelligence in the detection of skin cancer.

Recent advances in artificial intelligence (AI) in dermatology have demonstrated the potential to im...

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