AIMC Topic: Unsupervised Machine Learning

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Developing an Intelligent Automatic Appendix Extraction Method from Ultrasonography Based on Fuzzy ART and Image Processing.

Computational and mathematical methods in medicine
Ultrasound examination (US) does a key role in the diagnosis and management of the patients with clinically suspected appendicitis which is the most common abdominal surgical emergency. Among the various sonographic findings of appendicitis, outer di...

A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Adaptive radiation therapy (ART) is an advanced field of radiation oncology. Image-guided radiation therapy (IGRT) methods can support daily setup and assess anatomical variations during therapy, which could prevent incorrect dose distributi...

An unsupervised feature learning framework for basal cell carcinoma image analysis.

Artificial intelligence in medicine
OBJECTIVE: The paper addresses the problem of automatic detection of basal cell carcinoma (BCC) in histopathology images. In particular, it proposes a framework to both, learn the image representation in an unsupervised way and visualize discriminati...

Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Extracting medical knowledge from electronic medical records requires automated approaches to combat scalability limitations and selection biases. However, existing machine learning approaches are often regarded by clinicians as black boxe...

Integrating biological knowledge based on functional annotations for biclustering of gene expression data.

Computer methods and programs in biomedicine
Gene expression data analysis is based on the assumption that co-expressed genes imply co-regulated genes. This assumption is being reformulated because the co-expression of a group of genes may be the result of an independent activation with respect...

Molecular classification of amyotrophic lateral sclerosis by unsupervised clustering of gene expression in motor cortex.

Neurobiology of disease
Amyotrophic lateral sclerosis (ALS) is a rapidly progressive and ultimately fatal neurodegenerative disease, caused by the loss of motor neurons in the brain and spinal cord. Although 10% of ALS cases are familial (FALS), the majority are sporadic (S...

Inferring fungal cis-regulatory networks from genome sequences via unsupervised and interpretable representation learning.

Genetics
Gene expression patterns are determined to a large extent by transcription factor (TF) binding to noncoding regulatory regions in the genome. However, gene expression cannot yet be systematically predicted from genome sequences, in part because nonfu...

Complexity-based unsupervised machine learning for patient-specific VMAT quality assurance.

Medical physics
BACKGROUND: Patient-specific quality assurance (PSQA) is essential to guarantee the requested accuracy and safety of high-precision radiotherapy treatments. With the widespread adoption of modulated-intensity techniques, there is a growing need for i...

Unsupervised Machine Learning-Based Process Analytical Tools for Near Real-Time Cell Morphology Analysis During CAR-T Cell Manufacturing.

Biotechnology and bioengineering
Cell therapies like Chimeric Antigen Receptor (CAR)-T cell therapy deliver living cells to patients as active pharmaceutical ingredients. Manufacturing of these cells is complex, often yielding, heterogeneous products and high failure rates. Quality ...