AIMC Topic: Deep Learning

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Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learning.

Scientific reports
Tumour heterogeneity in breast cancer poses challenges in predicting outcome and response to therapy. Spatial transcriptomics technologies may address these challenges, as they provide a wealth of information about gene expression at the cell level, ...

Feature-aware unsupervised lesion segmentation for brain tumor images using fast data density functional transform.

Scientific reports
We demonstrate that isomorphically mapping gray-level medical image matrices onto energy spaces underlying the framework of fast data density functional transform (fDDFT) can achieve the unsupervised recognition of lesion morphology. By introducing t...

Deep learning-based postoperative visual acuity prediction in idiopathic epiretinal membrane.

BMC ophthalmology
BACKGROUND: To develop a deep learning (DL) model based on preoperative optical coherence tomography (OCT) training to automatically predict the 6-month postoperative visual outcomes in patients with idiopathic epiretinal membrane (iERM).

A subcomponent-guided deep learning method for interpretable cancer drug response prediction.

PLoS computational biology
Accurate prediction of cancer drug response (CDR) is a longstanding challenge in modern oncology that underpins personalized treatment. Current computational methods implement CDR prediction by modeling responses between entire drugs and cell lines, ...

De novo drug design based on patient gene expression profiles via deep learning.

Molecular informatics
Computational de novo drug design is a challenging issue in medicine, and it is desirable to consider all of the relevant information of the biological systems in a disease state. Here, we propose a novel computational method to generate drug candida...

Incremental learning for an evolving stream of medical ultrasound images via counterfactual thinking.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Despite the fact that traditional deep learning (DL) approaches provide promising accuracy and efficiency in medical ultrasound image analysis, they cannot replace the physician in making a diagnosis since the DL model is only appropriate in static a...

3D Breast Cancer Segmentation in DCE-MRI Using Deep Learning With Weak Annotation.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep learning models require large-scale training to perform confidently, but obtaining annotated datasets in medical imaging is challenging. Weak annotation has emerged as a way to save time and effort.

Noninvasive identification of HER2-low-positive status by MRI-based deep learning radiomics predicts the disease-free survival of patients with breast cancer.

European radiology
OBJECTIVE: This study aimed to establish a MRI-based deep learning radiomics (DLR) signature to predict the human epidermal growth factor receptor 2 (HER2)-low-positive status and further verified the difference in prognosis by the DLR model.

Multi-task deep learning-based radiomic nomogram for prognostic prediction in locoregionally advanced nasopharyngeal carcinoma.

European journal of nuclear medicine and molecular imaging
PURPOSE: Prognostic prediction is crucial to guide individual treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients. Recently, multi-task deep learning was explored for joint prognostic prediction and tumor segmentation in ...

CardioVision: A fully automated deep learning package for medical image segmentation and reconstruction generating digital twins for patients with aortic stenosis.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Aortic stenosis (AS) is the most prevalent heart valve disease in western countries that poses a significant public health challenge due to the lack of a medical treatment to prevent valve calcification. Given the aging population demographic, the pr...