Oncology/Hematology

Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features.

White Blood Cell (WBC) Leukaemia is caused by excessive production of leukocytes in the bone marrow, and image-based detection of malignant WBCs is important for its detection. Convolutional Neural Networks (CNNs) present the current state-of-the-art for this type of image classification, but their computational cost for training and deployment can be high. We here present an improved hybrid appro...

Machine Learning Algorithms for Predicting the Recurrence of Stage IV Colorectal Cancer After Tumor Resection.

The aim of this study is to explore the feasibility of using machine learning (ML) technology to predict postoperative recurrence risk among stage IV colorectal cancer patients. Four basic ML algorithms were used for prediction-logistic regression, decision tree, GradientBoosting and lightGBM. The research samples were randomly divided into a training group and a testing group at a ratio of 8:2. 9...

Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning.

To assist radiologists in breast cancer classification in automated breast ultrasound (ABUS) imaging, we propose a computer-aided diagnosis based on a...

RFDCR: Automated brain lesion segmentation using cascaded random forests with dense conditional random fields.

Segmentation of brain lesions from magnetic resonance images (MRI) is an important step for disease diagnosis, surgical planning, radiotherapy and che...

Application of Machine Learning in Developing a Novelty Five-Pseudogene Signature to Predict Prognosis of Head and Neck Squamous Cell Carcinoma: A New Aspect of "Junk Genes" in Biomedical Practice.

Head and neck squamous cell carcinoma (HNSCC) is the sixth malignancy, which is characterized by poor prognosis or high mortality because of the lack ...

Channel Attention Module With Multiscale Grid Average Pooling for Breast Cancer Segmentation in an Ultrasound Image.

Breast cancer accounts for the second-largest number of deaths in women around the world, and more than 8% of women will suffer from the disease in th...

Cell Image Classification: A Comparative Overview.

Cell image classification methods are currently being used in numerous applications in cell biology and medicine. Applications include understanding t...

Automated differentiation of benign renal oncocytoma and chromophobe renal cell carcinoma on computed tomography using deep learning.

OBJECTIVES: To develop and evaluate the feasibility of an objective method using artificial intelligence (AI) and image processing in a semi-automated...

A Translational Pipeline for Overall Survival Prediction of Breast Cancer Patients by Decision-Level Integration of Multi-Omics Data.

Breast cancer is the most prevalent and among the most deadly cancers in females. Patients with breast cancer have highly variable survival rates, ind...

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a pati...

SDCT-AuxNet: DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis.

Acute lymphoblastic leukemia (ALL) is a pervasive pediatric white blood cell cancer across the globe. With the popularity of convolutional neural netw...

A deep learning approach to radiation dose estimation.

Currently methods for predicting absorbed dose after administering a radiopharmaceutical are rather crude in daily clinical practice. Most importantly...

Application of a machine learning algorithm to predict malignancy in thyroid cytopathology.

BACKGROUND: The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) comprises 6 categories used for the diagnosis of thyroid fine-needle aspi...

Computational analysis of morphological and molecular features in gastric cancer tissues.

Biological morphologies of cells and tissues represent their physiological and pathological conditions. The importance of quantitative assessment of m...

Real-time colorectal cancer diagnosis using PR-OCT with deep learning.

Prior reports have shown optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering an alternativ...

User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions.

PURPOSE: Precision cancer medicine is dependent on accurate prediction of disease and treatment outcome, requiring integration of clinical, imaging an...

Factors Influencing Delivery of Cancer Survivorship Care Plans: A National Patterns of Care Study.

Nearly half of all Americans will develop cancer at least once in their lifetime. Through improved screening and treatments, the number of cancer surv...

Machine learning to predict early recurrence after oesophageal cancer surgery.

BACKGROUND: Early cancer recurrence after oesophagectomy is a common problem, with an incidence of 20-30 per cent despite the widespread use of neoadj...

Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning.

PURPOSE: Pancreatic cancer remains a disease of high mortality despite advanced diagnostic techniques. Mucins (MUC) play crucial roles in carcinogenes...

A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI.

BACKGROUND: Differentiate malignant from benign enhancing foci on breast magnetic resonance imaging (MRI) through radiomic signature.