AIMC Topic: Early Detection of Cancer

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Advanced deep learning algorithms in oral cancer detection: Techniques and applications.

Journal of environmental science and health. Part C, Toxicology and carcinogenesis
As the 16 most common cancer globally, oral cancer yearly accounts for some 355,000 new cases. This study underlines that an early diagnosis can improve the prognosis and cut down on mortality. It discloses a multifaceted approach to the detection of...

Investigating the key principles in two-step heterogeneous transfer learning for early laryngeal cancer identification.

Scientific reports
Data scarcity in medical images makes transfer learning a common approach in computer-aided diagnosis. Some disease classification tasks can rely on large homogeneous public datasets to train the transferred model, while others cannot, i.e., endoscop...

An Efficient Acute Lymphoblastic Leukemia Screen Framework Based on Multi-Modal Deep Neural Network.

International journal of laboratory hematology
BACKGROUND: Acute lymphoblastic leukemia (ALL) is a leading cause of death among pediatric malignancies. Early diagnosis of ALL is crucial for minimizing misdiagnosis, improving survival rates, and ensuring the implementation of precise treatment pla...

Blood cancer prediction model based on deep learning technique.

Scientific reports
Blood cancer is among the critical health concerns among people around the world and normally emanates from genetic and environmental issues. Early detection becomes essential, as the rate of death associated with it is high, to ensure that the rate ...

AI image analysis as the basis for risk-stratified screening.

Japanese journal of radiology
Artificial intelligence (AI) has emerged as a transformative tool in breast cancer screening, with two distinct applications: computer-aided cancer detection (CAD) and risk prediction. While AI CAD systems are slowly finding its way into clinical pra...

A benchmark of deep learning approaches to predict lung cancer risk using national lung screening trial cohort.

Scientific reports
Deep learning (DL) methods have demonstrated remarkable effectiveness in assisting with lung cancer risk prediction tasks using computed tomography (CT) scans. However, the lack of comprehensive comparison and validation of state-of-the-art (SOTA) mo...

Traditional versus modern approaches to screening mammography: a comparison of computer-assisted detection for synthetic 2D mammography versus an artificial intelligence algorithm for digital breast tomosynthesis.

Breast cancer research and treatment
PURPOSE: Traditional computer-assisted detection (CADe) algorithms were developed for 2D mammography, while modern artificial intelligence (AI) algorithms can be applied to 2D mammography and/or digital breast tomosynthesis (DBT). The objective is to...

Nationwide real-world implementation of AI for cancer detection in population-based mammography screening.

Nature medicine
Artificial intelligence (AI) in mammography screening has shown promise in retrospective evaluations, but few prospective studies exist. PRAIM is an observational, multicenter, real-world, noninferiority, implementation study comparing the performanc...

DIFLF: A domain-invariant features learning framework for single-source domain generalization in mammogram classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Single-source domain generalization (SSDG) aims to generalize a deep learning (DL) model trained on one source dataset to multiple unseen datasets. This is important for the clinical applications of DL-based models to breast...