AIMC Topic: Neoplasms

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The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.

Technology in cancer research & treatment
With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation o...

Brief Survey on Machine Learning in Epistasis.

Methods in molecular biology (Clifton, N.J.)
In biology, the term "epistasis" indicates the effect of the interaction of a gene with another gene. A gene can interact with an independently sorted gene, located far away on the chromosome or on an entirely different chromosome, and this interacti...

Overcoming Interpretability in Deep Learning Cancer Classification.

Methods in molecular biology (Clifton, N.J.)
Since its inception, deep learning has revolutionized the field of machine learning and data-driven science. One such data-driven science to be transformed by deep learning is genomics. In the past decade, numerous genomics studies have adopted deep ...

Chemotherapy Knowledge Base Management in the Era of Precision Oncology.

JCO clinical cancer informatics
Cancer medicine has grown increasingly complex in recent years with the advent of precision oncology and wide utilization of multidrug regimens. Representing this increasingly granular knowledge is a significant challenge. As users and managers of a ...

A Review on Multi-organ Cancer Detection Using Advanced Machine Learning Techniques.

Current medical imaging
Abnormal behaviors of tumors pose a risk to human survival. Thus, the detection of cancers at their initial stage is beneficial for patients and lowers the mortality rate. However, this can be difficult due to various factors related to imaging modal...

Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models.

Methods in molecular biology (Clifton, N.J.)
Intrinsic and acquired drug resistance is a major challenge in cancer therapy. Synergistic drug combinations could help to overcome drug resistance. However, the number of possible drug combinations is enormous, and it is infeasible to experimentally...

Applying Machine Learning for Integration of Multi-Modal Genomics Data and Imaging Data to Quantify Heterogeneity in Tumour Tissues.

Methods in molecular biology (Clifton, N.J.)
With rapid advances in experimental instruments and protocols, imaging and sequencing data are being generated at an unprecedented rate contributing significantly to the current and coming big biomedical data. Meanwhile, unprecedented advances in com...

Edge detection algorithm of cancer image based on deep learning.

Bioengineered
For the existing medical image edge detection algorithm image reconstruction accuracy is not high, the fitness of optimization coefficient is low, resulting in the detection results of low information recall, poor smoothness and low detection accurac...

Deep learning of pharmacogenomics resources: moving towards precision oncology.

Briefings in bioinformatics
The recent accumulation of cancer genomic data provides an opportunity to understand how a tumor's genomic characteristics can affect its responses to drugs. This field, called pharmacogenomics, is a key area in the development of precision oncology....

[Artificial intelligence and machine learning in oncologic imaging].

Der Pathologe
Machine learning (ML) is entering many areas of society, including medicine. This transformation has the potential to drastically change medicine and medical practice. These aspects become particularly clear when considering the different stages of o...