Oncology/Hematology

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

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Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics.

Embeddings derived from cell graphs hold significant potential for exploring spatial transcriptomics...

Analysis of Cancer-Associated Mutations of POLB Using Machine Learning and Bioinformatics.

DNA damage is a critical factor in the onset and progression of cancer. When DNA is damaged, the num...

SGLMDA: A Subgraph Learning-Based Method for miRNA-Disease Association Prediction.

MicroRNAs (miRNA) are endogenous non-coding RNAs, typically around 23 nucleotides in length. Many mi...

Graph neural networks in multi-stained pathological imaging: extended comparative analysis of Radiomic features.

PURPOSE: This study investigates the application of Radiomic features within graph neural networks (...

A review of deep learning approaches for multimodal image segmentation of liver cancer.

This review examines the recent developments in deep learning (DL) techniques applied to multimodal ...

Ranking attention multiple instance learning for lymph node metastasis prediction on multicenter cervical cancer MRI.

PURPOSE: In the current clinical diagnostic process, the gold standard for lymph node metastasis (LN...

Prediction of pathological complete response to chemotherapy for breast cancer using deep neural network with uncertainty quantification.

BACKGROUND: The I-SPY 2 trial is a national-wide, multi-institutional clinical trial designed to eva...

Artificial intelligence for detection and characterization of focal hepatic lesions: a review.

Focal liver lesions (FLL) are common incidental findings in abdominal imaging. While the majority of...

MixUNETR: A U-shaped network based on W-MSA and depth-wise convolution with channel and spatial interactions for zonal prostate segmentation in MRI.

Magnetic resonance imaging (MRI) plays a pivotal role in diagnosing and staging prostate cancer. Pre...

A survey on cell nuclei instance segmentation and classification: Leveraging context and attention.

Nuclear-derived morphological features and biomarkers provide relevant insights regarding the tumour...

Ensemble approach of deep learning models for binary and multiclass classification of histopathological images for breast cancer.

Breast cancer (BC) is the most frequently occurring cancer disease observed in women after lung canc...

Enhanced cancer classification and critical feature visualization using Raman spectroscopy and convolutional neural networks.

Cell misuse and cross-contamination pose a significant threat to the accuracy of cell research outco...

Prognostic insights after surgery for advances in understanding signet ring cell gastric cancer: a machine learning approach.

BACKGROUND: Signet ring cell (SRC) gastric carcinoma is traditionally associated with a poor prognos...

A Lightweight Method for Breast Cancer Detection Using Thermography Images with Optimized CNN Feature and Efficient Classification.

Breast cancer is a prominent cause of death among women worldwide. Infrared thermography, due to its...

Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature.

Interventional oncology provides image-guided therapies, including transarterial tumor embolization ...

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