AIMC Topic: Deep Learning

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Deep learning-based grading of ductal carcinoma in situ in breast histopathology images.

Laboratory investigation; a journal of technical methods and pathology
Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesio...

Training deep-learning segmentation models from severely limited data.

Medical physics
PURPOSE: To enable generation of high-quality deep learning segmentation models from severely limited contoured cases (e.g., ~10 cases).

Deep learning based automated diagnosis of bone metastases with SPECT thoracic bone images.

Scientific reports
SPECT nuclear medicine imaging is widely used for treating, diagnosing, evaluating and preventing various serious diseases. The automated classification of medical images is becoming increasingly important in developing computer-aided diagnosis syste...

An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning.

Nature communications
Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole-slide images (WSIs). Most studies have employed patch-based methods, which often require detailed annotation of image patches. This typically involves l...

Deep learning the collisional cross sections of the peptide universe from a million experimental values.

Nature communications
The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To investigate the nature and utility of the peptide collisional cross section (CCS) space, we measure more than a million dat...

A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network.

eLife
Forests provide biodiversity, ecosystem, and economic services. Information on individual trees is important for understanding forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data...

A deep learning approach for lower back-pain risk prediction during manual lifting.

PloS one
Occupationally-induced back pain is a leading cause of reduced productivity in industry. Detecting when a worker is lifting incorrectly and at increased risk of back injury presents significant possible benefits. These include increased quality of li...

Performance of a deep-learning algorithm for referable thoracic abnormalities on chest radiographs: A multicenter study of a health screening cohort.

PloS one
PURPOSE: This study evaluated the performance of a commercially available deep-learning algorithm (DLA) (Insight CXR, Lunit, Seoul, South Korea) for referable thoracic abnormalities on chest X-ray (CXR) using a consecutively collected multicenter hea...

Deep Learning Approach for Automated Detection of Myopic Maculopathy and Pathologic Myopia in Fundus Images.

Ophthalmology. Retina
PURPOSE: To determine whether eyes with pathologic myopia can be identified and whether each type of myopic maculopathy lesion on fundus photographs can be diagnosed by deep learning (DL) algorithms.

Prediction of specialty coffee flavors based on near-infrared spectra using machine- and deep-learning methods.

Journal of the science of food and agriculture
BACKGROUND: Specialty coffee fascinates people with its bountiful flavors. Currently, flavor descriptions of specialty coffee beans are only offered by certified coffee cuppers. However, such professionals are rare, and the market demand is tremendou...