AIMC Topic: Humans

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Using deep learning to predict survival outcome in non-surgical cervical cancer patients based on pathological images.

Journal of cancer research and clinical oncology
PURPOSE: We analyzed clinical features and the representative HE-stained pathologic images to predict 5-year overall survival via the deep-learning approach in cervical cancer patients in order to assist oncologists in designing the optimal treatment...

Deeper neural network models better reflect how humans cope with contrast variation in object recognition.

Neuroscience research
Visual inputs are far from ideal in everyday situations such as in the fog where the contrasts of input stimuli are low. However, human perception remains relatively robust to contrast variations. To provide insights about the underlying mechanisms o...

Using Transfer Learning of Convolutional Neural Network on Neck Radiographs to Identify Acute Epiglottitis.

Journal of digital imaging
Acute epiglottitis (AE) is a life-threatening condition and needs to be recognized timely. Diagnosis of AE with a lateral neck radiograph yields poor reliability and sensitivity. Convolutional neural networks (CNN) are powerful tools to assist the an...

Rapid high-fidelity mapping using single-shot overlapping-echo acquisition and deep learning reconstruction.

Magnetic resonance in medicine
PURPOSE: To develop and evaluate a single-shot quantitative MRI technique called GRE-MOLED (gradient-echo multiple overlapping-echo detachment) for rapid mapping.

Diffusion MRI data analysis assisted by deep learning synthesized anatomical images (DeepAnat).

Medical image analysis
Diffusion MRI is a useful neuroimaging tool for non-invasive mapping of human brain microstructure and structural connections. The analysis of diffusion MRI data often requires brain segmentation, including volumetric segmentation and cerebral cortic...

Use of machine learning in pediatric surgical clinical prediction tools: A systematic review.

Journal of pediatric surgery
PURPOSE: Clinical prediction tools (CPTs) are decision-making instruments utilizing patient data to predict specific clinical outcomes, risk-stratify patients, or suggest personalized diagnostic or therapeutic options. Recent advancements in artifici...

Evaluation of generalization ability for deep learning-based auto-segmentation accuracy in limited field of view CBCT of male pelvic region.

Journal of applied clinical medical physics
PURPOSE: The aim of this study was to evaluate generalization ability of segmentation accuracy for limited FOV CBCT in the male pelvic region using a full-image CNN. Auto-segmentation accuracy was evaluated using various datasets with different inten...

Artificial intelligence in radiology: trainees want more.

Clinical radiology
AIM: To understand the attitudes of UK radiology trainees towards artificial intelligence (AI) in Radiology, in particular, assessing the demand for AI education.

Machine Learning Models for Predicting the Outcomes of Surgical Treatment of Colorectal Liver Metastases.

Journal of the American College of Surgeons
BACKGROUND: Surgical intervention remains the cornerstone of a multidisciplinary approach in the treatment of colorectal liver metastases (CLM). Nevertheless, patient outcomes vary greatly. While predictive tools can assist decision-making and patien...

Handcrafted Histological Transformer (H2T): Unsupervised representation of whole slide images.

Medical image analysis
Diagnostic, prognostic and therapeutic decision-making of cancer in pathology clinics can now be carried out based on analysis of multi-gigapixel tissue images, also known as whole-slide images (WSIs). Recently, deep convolutional neural networks (CN...