AIMC Topic: Deep Learning

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Markerless 2D kinematic analysis of underwater running: A deep learning approach.

Journal of biomechanics
Kinematic analysis is often performed with a camera system combined with reflective markers placed over bony landmarks. This method is restrictive (and often expensive), and limits the ability to perform analyses outside of the lab. In the present st...

Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs.

JAMA network open
IMPORTANCE: Interpretation of chest radiographs is a challenging task prone to errors, requiring expert readers. An automated system that can accurately classify chest radiographs may help streamline the clinical workflow.

Assessment of a Deep Learning Model Based on Electronic Health Record Data to Forecast Clinical Outcomes in Patients With Rheumatoid Arthritis.

JAMA network open
IMPORTANCE: Knowing the future condition of a patient would enable a physician to customize current therapeutic options to prevent disease worsening, but predicting that future condition requires sophisticated modeling and information. If artificial ...

CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture.

Sensors (Basel, Switzerland)
Intelligence has been considered as the major challenge in promoting economic potential and production efficiency of precision agriculture. In order to apply advanced deep-learning technology to complete various agricultural tasks in online and offli...

Deep learning for predicting toxicity of chemicals: a mini review.

Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews
Humans and wildlife inhabit a world with panoply of natural and synthetic chemicals. Alarmingly, only a limited number of chemicals have undergone comprehensive toxicological evaluation due to limitations of traditional toxicity testing. High-through...

U-Net based deep learning bladder segmentation in CT urography.

Medical physics
OBJECTIVES: To develop a U-Net-based deep learning approach (U-DL) for bladder segmentation in computed tomography urography (CTU) as a part of a computer-assisted bladder cancer detection and treatment response assessment pipeline.

Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis.

Trends in cancer
Deep learning refers to a set of computer models that have recently been used to make unprecedented progress in the way computers extract information from images. These algorithms have been applied to tasks in numerous medical specialties, most exten...

Radiological images and machine learning: Trends, perspectives, and prospects.

Computers in biology and medicine
The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years. Recent advances in machine learning have the potential to recognize and classify complex patter...

Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge.

Medical image analysis
Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor prolifera...

Molecular Structure Extraction from Documents Using Deep Learning.

Journal of chemical information and modeling
Chemical structure extraction from documents remains a hard problem because of both false positive identification of structures during segmentation and errors in the predicted structures. Current approaches rely on handcrafted rules and subroutines t...