AIMC Topic: Deep Learning

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The pathological risk score: A new deep learning-based signature for predicting survival in cervical cancer.

Cancer medicine
PURPOSE: To develop and validate a deep learning-based pathological risk score (RS) with an aim of predicting patients' prognosis to investigate the potential association between the information within the whole slide image (WSI) and cervical cancer ...

Deep Learning-based Post Hoc CT Denoising for Myocardial Delayed Enhancement.

Radiology
Background To improve myocardial delayed enhancement (MDE) CT, a deep learning (DL)-based post hoc denoising method supervised with averaged MDE CT data was developed. Purpose To assess the image quality of denoised MDE CT images and evaluate their d...

Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT.

International journal of computer assisted radiology and surgery
PURPOSE: Although surgery is the primary treatment for lung cancer, some patients experience recurrence at a certain rate. If postoperative recurrence can be predicted early before treatment is initiated, it may be possible to provide individualized ...

CM-SegNet: A deep learning-based automatic segmentation approach for medical images by combining convolution and multilayer perceptron.

Computers in biology and medicine
Accurate segmentation of lesions in medical images is of great significance for clinical diagnosis and evaluation. The low contrast between lesions and surrounding tissues increases the difficulty of automatic segmentation, while the efficiency of ma...

HunCRC: annotated pathological slides to enhance deep learning applications in colorectal cancer screening.

Scientific data
Histopathology is the gold standard method for staging and grading human tumors and provides critical information for the oncoteam's decision making. Highly-trained pathologists are needed for careful microscopic analysis of the slides produced from ...

Logistic Regression-Based Model Is More Efficient Than U-Net Model for Reliable Whole Brain Magnetic Resonance Imaging Segmentation.

Topics in magnetic resonance imaging : TMRI
OBJECTIVES: Automated whole brain segmentation from magnetic resonance images is of great interest for the development of clinically relevant volumetric markers for various neurological diseases. Although deep learning methods have demonstrated remar...

Research on Image Recognition of Gymnastics Sports Injuries Based on Deep Learning.

Computational intelligence and neuroscience
Gymnastics is an increasingly popular sport and an important event in the Olympic Games. However, the number of unavoidable injuries in sports is also increasing, and the treatment after the injury is very important. We reduce the harm caused by the ...

Research on Online Social Network Information Leakage-Tracking Algorithm Based on Deep Learning.

Computational intelligence and neuroscience
The rapid iteration of information technology makes the development of online social networks increasingly rapid, and its corresponding network scale is also increasingly large and complex. The corresponding algorithms to deal with social networks an...