AIMC Topic: Deep Learning

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Lung_PAYNet: a pyramidal attention based deep learning network for lung nodule segmentation.

Scientific reports
Accurate and reliable lung nodule segmentation in computed tomography (CT) images is required for early diagnosis of lung cancer. Some of the difficulties in detecting lung nodules include the various types and shapes of lung nodules, lung nodules ne...

Personalized synthetic MR imaging with deep learning enhancements.

Magnetic resonance in medicine
PURPOSE: Personalized synthetic MRI (syn-MRI) uses MR images of an individual subject acquired at a few design parameters (echo time, repetition time, flip angle) to obtain underlying parametric maps, from where MR images of that individual at other...

Deep-learning-based automatic evaluation of rice seed germination rate.

Journal of the science of food and agriculture
BACKGROUND: Rice is an important food crop plant in the world and is also a model plant for genetics and breeding research. The germination rate is an important indicator that measures the performance of rice seeds. Currently, solutions involving ima...

Deep learning model based on contrast-enhanced ultrasound for predicting early recurrence after thermal ablation of colorectal cancer liver metastasis.

European radiology
OBJECTIVES: To develop and validate a deep learning (DL) model based on quantitative analysis of contrast-enhanced ultrasound (CEUS) images that predicts early recurrence (ER) after thermal ablation (TA) of colorectal cancer liver metastasis (CRLM).

Multi-site cross-organ calibrated deep learning (MuSClD): Automated diagnosis of non-melanoma skin cancer.

Medical image analysis
Although deep learning (DL) has demonstrated impressive diagnostic performance for a variety of computational pathology tasks, this performance often markedly deteriorates on whole slide images (WSI) generated at external test sites. This phenomenon ...

Characterization of signature trends across the spectrum of non-alcoholic fatty liver disease using deep learning method.

Life sciences
AIMS: The timely diagnosis of different stages in NAFLD is crucial for disease treatment and reversal. We used hepatocellular ballooning to determine different NAFLD stages.

A practical guide to the development and deployment of deep learning models for the Orthopedic surgeon: part I.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Deep learning has a profound impact on daily life. As Orthopedics makes use of this rapid escalation in technology, Orthopedic surgeons will need to take leadership roles on deep learning projects. Moreover, surgeons must possess an understanding of ...

Portable, Automated and Deep-Learning-Enabled Microscopy for Smartphone-Tethered Optical Platform Towards Remote Homecare Diagnostics: A Review.

Small methods
Globally new pandemic diseases induce urgent demands for portable diagnostic systems to prevent and control infectious diseases. Smartphone-based portable diagnostic devices are significantly efficient tools to user-friendly connect personalized heal...

Explainability and controllability of patient-specific deep learning with attention-based augmentation for markerless image-guided radiotherapy.

Medical physics
BACKGROUND: We reported the concept of patient-specific deep learning (DL) for real-time markerless tumor segmentation in image-guided radiotherapy (IGRT). The method was aimed to control the attention of convolutional neural networks (CNNs) by artif...

A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with subframe temporal resolution.

Nature neuroscience
In many areas of the brain, neural populations act as a coordinated network whose state is tied to behavior on a millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe such network-scale phenomena. However, estimating the ...