Oncology/Hematology

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

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Deep neural network models for cell type prediction based on single-cell Hi-C data.

BACKGROUND: Cell type prediction is crucial to cell type identification of genomics, cancer diagnosi...

Leveraging immuno-fluorescence data to reduce pathologist annotation requirements in lung tumor segmentation using deep learning.

The main bottleneck in training a robust tumor segmentation algorithm for non-small cell lung cancer...

Use machine learning to predict pulmonary metastasis of esophageal cancer: a population-based study.

BACKGROUND: This study aims to establish a predictive model for assessing the risk of esophageal can...

An enhanced Garter Snake Optimization-assisted deep learning model for lung cancer segmentation and classification using CT images.

An early detection of lung tumors is critical for better treatment results, and CT scans can reveal ...

Antiproliferative and Pro-apoptotic Activities of vent. Leaves on the Human Breast Adenocarcinoma Cell Line (MCF-7).

BACKGROUND: Breast cancer is the most common cancer among women worldwide, impacting not only the pa...

Pulmonary nodule visualization and evaluation of AI-based detection at various ultra-low-dose levels using photon-counting detector CT.

BACKGROUND: Radiation dose should be as low as reasonably achievable. With the invention of photon-c...

Development and validation of machine learning models for predicting cancer-related fatigue in lymphoma survivors.

BACKGROUND: New cases of lymphoma are rising, and the symptom burden, like cancer-related fatigue (C...

Multitask machine learning-based tumor-associated collagen signatures predict peritoneal recurrence and disease-free survival in gastric cancer.

BACKGROUND: Accurate prediction of peritoneal recurrence for gastric cancer (GC) is crucial in clini...

Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach.

BACKGROUND: To design a pulmonary ground-glass nodules (GGN) classification method based on computed...

Convolutional Neural Networks for Segmentation of Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance).

The purpose of this study was to evaluate the impact of probability map threshold on pleural mesothe...

Bioinformatics challenges for profiling the microbiome in cancer: pitfalls and opportunities.

Increasing evidence suggests that the human microbiome plays an important role in cancer risk and tr...

Upconversion and NIR-II luminescent rare earth nanoparticles combined with machine learning for cancer theranostics.

How to develop contrast agents for cancer theranostics is a meaningful and challenging endeavor, and...

Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology.

OBJECTIVE: This study aimed to assess reporting quality of machine learning (ML) algorithms in the h...

Towards explainable oral cancer recognition: Screening on imperfect images via Informed Deep Learning and Case-Based Reasoning.

Oral squamous cell carcinoma recognition presents a challenge due to late diagnosis and costly data ...

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