Oncology/Hematology

Rapid assessment of cosmic radiation exposure in aviation based on BP neural network method.

Cosmic radiation exposure is one of the important health concerns for aircrews. In this work, we constructed a back propagation neural network model for the real-time and rapid assessment of cosmic radiation exposure to the public in aviation. The multi-dimensional dataset for this neural network was created from modeling the process of cosmic ray transportation in magnetic field by geomagnetic cu...

Development and Validation of Fully Automatic Deep Learning-Based Algorithms for Immunohistochemistry Reporting of Invasive Breast Ductal Carcinoma

Immunohistochemistry (IHC) analysis is a well-accepted and widely used method for molecular subtyping, a procedure for prognosis and targeted therapy of breast carcinoma, the most common type of tumor affecting women. There are four molecular biomarkers namely progesterone receptor (PR), estrogen receptor (ER), antigen Ki67, and human epidermal growth factor receptor 2 (HER2) whose assessment is...

MMIL: A novel algorithm for disease associated cell type discovery

Single-cell datasets often lack individual cell labels, making it challenging to identify cells associated with disease. To address this, we introdu...

[Artificial intelligence research advances in discrimination and diagnosis of pulmonary ground-glass nodules].

Lung cancer, which accounts for about 18% of all cancer-related deaths worldwide, has a dismal 5-year survival rate of less than 20%. Survival rates f...

Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes

Cancer clinics capture disease data at various scales, from genetic to organ level. Current bioinformatic methods struggle to handle the heterogeneo...

Research on Tumors Segmentation based on Image Enhancement Method

One of the most effective ways to treat liver cancer is to perform precise liver resection surgery, the key step of which includes precise digital i...

Development and Validation of a Deep-Learning Model for Differential Treatment Benefit Prediction for Adults with Major Depressive Disorder Deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study

INTRODUCTION: The pharmacological treatment of Major Depressive Disorder (MDD) relies on a trial-and-error approach. We introduce an artificial inte...

The Immunopeptidomics Ontology (ImPO).

The adaptive immune response plays a vital role in eliminating infected and aberrant cells from the body. This process hinges on the presentation of s...

Evaluating the Influence of Temporal Context on Automatic Mouse Sleep Staging through the Application of Human Models

In human sleep staging models, augmenting the temporal context of the input to the range of tens of minutes has recently demonstrated performance im...

The Deep Learning Framework iCanTCR Enables Early Cancer Detection Using the T-cell Receptor Repertoire in Peripheral Blood.

UNLABELLED: T cells recognize tumor antigens and initiate an anticancer immune response in the very early stages of tumor development, and the antigen...

Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy.

UNLABELLED: Standard-of-care treatment regimens have long been designed for maximal cell killing, yet these strategies often fail when applied to meta...

How Artificial Intelligence Unravels the Complex Web of Cancer Drug Response.

The intersection of precision medicine and artificial intelligence (AI) holds profound implications for cancer treatment, with the potential to signif...

Immunocto: a massive immune cell database auto-generated for histopathology

With the advent of novel cancer treatment options such as immunotherapy, studying the tumour immune micro-environment (TIME) is crucial to inform on...

Unraveling the complexity of the senescence-associated secretory phenotype in adamantinomatous craniopharyngioma using multimodal machine learning analysis.

BACKGROUND: Cellular senescence can have positive and negative effects on the body, including aiding in damage repair and facilitating tumor growth. A...

Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.

UNLABELLED: Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highl...

A Deep Learning Model for Cancer Type Prediction Sets a New Standard.

Classifying tumor types using machine learning approaches is not always trivial, particularly for challenging cases such as cancers of unknown primary...

A Diagnostic Model for Acute Lymphoblastic Leukemia Using Metaheuristics and Deep Learning Methods

Acute lymphoblastic leukemia (ALL) severity is determined by the presence and ratios of blast cells (abnormal white blood cells) in both bone marrow...

Artificial intelligence-based application in multiple myeloma.

Familiarity, confidence and preference of artificial intelligence feedback and prompts by Australian breast cancer screening readers.

Objectives This study explored the familiarity, perceptions and confidence of Australian radiology clinicians involved in reading screening mammograms...

Hybrid machine learning-based breast cancer segmentation framework using ultrasound images with optimal weighted features.

One of the most dangerous conditions in clinical practice is breast cancer because it affects the entire life of women in recent days. Nevertheless, t...