Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L) 1 blockade in patients with non-small cell lung cancer
Immunotherapy is used to treat almost all patients with advanced non-small cell lung cancer
(NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we …
(NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we …
Prediction of on-target and off-target activity of CRISPR–Cas13d guide RNAs using deep learning
HH Wessels, A Stirn, A Méndez-Mancilla, EJ Kim… - Nature …, 2024 - nature.com
Transcriptome engineering applications in living cells with RNA-targeting CRISPR effectors
depend on accurate prediction of on-target activity and off-target avoidance. Here we design …
depend on accurate prediction of on-target activity and off-target avoidance. Here we design …
Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
J Irvin, P Rajpurkar, M Ko, Y Yu, S Ciurea-Ilcus… - Proceedings of the AAAI …, 2019 - aaai.org
Large, labeled datasets have driven deep learning methods to achieve expert-level
performance on a variety of medical imaging tasks. We present CheXpert, a large dataset …
performance on a variety of medical imaging tasks. We present CheXpert, a large dataset …
Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification
The worldwide spread of coronavirus disease (COVID-19) has become a threat to global
public health. It is of great importance to rapidly and accurately screen and distinguish …
public health. It is of great importance to rapidly and accurately screen and distinguish …
Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet
Background Magnetic resonance imaging (MRI) of the knee is the preferred method for
diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject …
diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject …
The effect of image resolution on deep learning in radiography
CF Sabottke, BM Spieler - Radiology: Artificial Intelligence, 2020 - pubs.rsna.org
Purpose To examine variations of convolutional neural network (CNN) performance for
multiple chest radiograph diagnoses and image resolutions. Materials and Methods This …
multiple chest radiograph diagnoses and image resolutions. Materials and Methods This …
A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI
Multiparametric magnetic resonance imaging (mpMRI) has been shown to improve
radiologists' performance in the clinical diagnosis of breast cancer. This machine learning …
radiologists' performance in the clinical diagnosis of breast cancer. This machine learning …
The false positive problem of automatic bot detection in social science research
A Rauchfleisch, J Kaiser - PloS one, 2020 - journals.plos.org
The identification of bots is an important and complicated task. The bot classifier" Botometer"
was successfully introduced as a way to estimate the number of bots in a given list of …
was successfully introduced as a way to estimate the number of bots in a given list of …
Predicting HLA class II antigen presentation through integrated deep learning
Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II
molecules would be valuable for vaccine development and cancer immunotherapies …
molecules would be valuable for vaccine development and cancer immunotherapies …
Artificial intelligence based medical decision support system for early and accurate breast cancer prediction
Feature selection, which picks the optimal subset of characteristics related to the target data
by deleting unnecessary data, is one of the most important aspects of the machine learning …
by deleting unnecessary data, is one of the most important aspects of the machine learning …