AIMC Topic: Deep Learning

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A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea.

BMC medical informatics and decision making
BACKGROUND: Clinical Decision Support Systems (CDSSs) have recently attracted attention as a method for minimizing medical errors. Existing CDSSs are limited in that they do not reflect actual data. To overcome this limitation, we propose a CDSS base...

Deep Learning to Estimate RECIST in Patients with NSCLC Treated with PD-1 Blockade.

Cancer discovery
Real-world evidence (RWE), conclusions derived from analysis of patients not treated in clinical trials, is increasingly recognized as an opportunity for discovery, to reduce disparities, and to contribute to regulatory approval. Maximal value of RWE...

Detecting mouse squamous cell carcinoma from submicron full-field optical coherence tomography images by deep learning.

Journal of biophotonics
The standard medical practice for cancer diagnosis requires histopathology, which is an invasive and time-consuming procedure. Optical coherence tomography (OCT) is an alternative that is relatively fast, noninvasive, and able to capture three-dimens...

COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images.

Interdisciplinary sciences, computational life sciences
The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a major pandemic outbreak recently. Various diagnostic technologies have been under active development. The novel coronavirus disease (COVID-19) may induce ...

The utility of a deep learning-based algorithm for bone scintigraphy in patient with prostate cancer.

Annals of nuclear medicine
OBJECTIVE: Bone scintigraphy has often been used to evaluate bone metastases. Its functionality is evident in detecting bone metastasis in patients with malignant tumor including prostate cancer, as appropriate treatment and prognosis are dependent o...

Radiomics for Gleason Score Detection through Deep Learning.

Sensors (Basel, Switzerland)
Prostate cancer is classified into different stages, each stage is related to a different Gleason score. The labeling of a diagnosed prostate cancer is a task usually performed by radiologists. In this paper we propose a deep architecture, based on s...

De-identifying free text of Japanese electronic health records.

Journal of biomedical semantics
BACKGROUND: Recently, more electronic data sources are becoming available in the healthcare domain. Electronic health records (EHRs), with their vast amounts of potentially available data, can greatly improve healthcare. Although EHR de-identificatio...

Influence of medical domain knowledge on deep learning for Alzheimer's disease prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) is the most common type of dementia that can seriously affect a person's ability to perform daily activities. Estimates indicate that AD may rank third as a cause of death for older people, after hea...

High-content image generation for drug discovery using generative adversarial networks.

Neural networks : the official journal of the International Neural Network Society
Immense amount of high-content image data generated in drug discovery screening requires computationally driven automated analysis. Emergence of advanced machine learning algorithms, like deep learning models, has transformed the interpretation and a...

Deep learning object detection of maxillary cyst-like lesions on panoramic radiographs: preliminary study.

Oral radiology
OBJECTIVES: This study aimed to examine the performance of deep learning object detection technology for detecting and identifying maxillary cyst-like lesions on panoramic radiography.