AIMC Topic: Humans

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Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty.

Medical image analysis
The clinical interest is often to measure the volume of a structure, which is typically derived from a segmentation. In order to evaluate and compare segmentation methods, the similarity between a segmentation and a predefined ground truth is measure...

Efficient and Robust Instrument Segmentation in 3D Ultrasound Using Patch-of-Interest-FuseNet with Hybrid Loss.

Medical image analysis
Instrument segmentation plays a vital role in 3D ultrasound (US) guided cardiac intervention. Efficient and accurate segmentation during the operation is highly desired since it can facilitate the operation, reduce the operational complexity, and the...

Modelling the effects of transcranial alternating current stimulation on the neural encoding of speech in noise.

NeuroImage
Transcranial alternating current stimulation (tACS) can non-invasively modulate neuronal activity in the cerebral cortex, in particular at the frequency of the applied stimulation. Such modulation can matter for speech processing, since the latter in...

DeepResp: Deep learning solution for respiration-induced B fluctuation artifacts in multi-slice GRE.

NeuroImage
Respiration-induced B fluctuation corrupts MRI images by inducing phase errors in k-space. A few approaches such as navigator have been proposed to correct for the artifacts at the expense of sequence modification. In this study, a new deep learning ...

Deep learning in glaucoma with optical coherence tomography: a review.

Eye (London, England)
Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has made significant breakthroughs in medical imaging, particularly for image classification and pattern recognition. In ophthalmology, applying DL for glauco...

Improving CNN training on endoscopic image data by extracting additionally training data from endoscopic videos.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In this work we present a technique to deal with one of the biggest problems for the application of convolutional neural networks (CNNs) in the area of computer assisted endoscopic image diagnosis, the insufficient amount of training data. Based on p...

Deep learning-enabled MRI-only photon and proton therapy treatment planning for paediatric abdominal tumours.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSE: To assess the feasibility of magnetic resonance imaging (MRI)-only treatment planning for photon and proton radiotherapy in children with abdominal tumours.

Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques.

Cancer reports (Hoboken, N.J.)
BACKGROUND: Oral squamous cell carcinoma (OSCC) is the most prevalent form of oral cancer. Very few researches have been carried out for the automatic diagnosis of OSCC using artificial intelligence techniques. Though biopsy is the ultimate test for ...

Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design.

Artificial intelligence in medicine
OBJECTIVE: Optimizing timing of defibrillation by evaluating the likelihood of a successful outcome could significantly enhance resuscitation. Previous studies employed conventional machine learning approaches and hand-crafted features to address thi...