AIMC Topic: Deep Learning

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Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.

Nature communications
Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value f...

Ultra-fast deep-learned CNS tumour classification during surgery.

Nature
Central nervous system tumours represent one of the most lethal cancer types, particularly among children. Primary treatment includes neurosurgical resection of the tumour, in which a delicate balance must be struck between maximizing the extent of r...

Automatic identification and morphological comparison of bivalve and brachiopod fossils based on deep learning.

PeerJ
Fossil identification is an essential and fundamental task for conducting palaeontological research. Because the manual identification of fossils requires extensive experience and is time-consuming, automatic identification methods are proposed. Howe...

A preliminary exploration into top-down and bottom-up deep-learning approaches to localising neuro-interventional point targets in volumetric MRI.

International journal of computer assisted radiology and surgery
PURPOSE: Point localisation is a critical aspect of many interventional planning procedures, specifically representing anatomical regions of interest or landmarks as individual points. This could be seen as analogous to the problem of visual search i...

Volumetric tumor tracking from a single cone-beam X-ray projection image enabled by deep learning.

Medical image analysis
Radiotherapy serves as a pivotal treatment modality for malignant tumors. However, the accuracy of radiotherapy is significantly compromised due to respiratory-induced fluctuations in the size, shape, and position of the tumor. To address this challe...

Built to Last? Reproducibility and Reusability of Deep Learning Algorithms in Computational Pathology.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Recent progress in computational pathology has been driven by deep learning. While code and data availability are essential to reproduce findings from preceding publications, ensuring a deep learning model's reusability is more challenging. For that,...

Machine Learning and Omics Analysis in Aortic Aneurysm.

Angiology
Aortic aneurysm is a life-threatening condition and mechanisms underlying its formation and progression are still incompletely understood. Omics approach has brought new insights to identify a broad spectrum of biomarkers and better understand cellul...

Deep learning for estimation of fetal weight throughout the pregnancy from fetal abdominal ultrasound.

American journal of obstetrics & gynecology MFM
BACKGROUND: Fetal weight is currently estimated from fetal biometry parameters using heuristic mathematical formulas. Fetal biometry requires measurements of the fetal head, abdomen, and femur. However, this examination is prone to inter- and intraob...

Deep learning in endoscopy: the importance of standardisation.

Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale

Speech and language processing with deep learning for dementia diagnosis: A systematic review.

Psychiatry research
Dementia is a progressive neurodegenerative disease that burdens the person living with the disease, their families, and medical and social services. Timely diagnosis of dementia could be followed by introducing interventions that may slow down its p...