Cardiovascular

A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images.

MOTIVATION: Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) patients accurately is direly needed for clinical decision making. pCR is also regarded as a strong predictor of overall survival. In this work, we propose a deep learning system to predict pCR to NAC based on serial pathology images stained with hematoxylin and eos...

Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study.

BACKGROUND: Artificial intelligence (AI) and deep learning have shown great potential in streamlining clinical tasks. However, most studies remain confined to in silico validation in small internal cohorts, without external validation or data on real-world clinical utility. We developed a strategy for the clinical validation of deep learning models for segmenting primary non-small-cell lung cancer...

Complex dynamics in a Hopfield neural network under electromagnetic induction and electromagnetic radiation.

Due to the potential difference between two neurons and that between the inner and outer membranes of an individual neuron, the neural network is alwa...

A Cascaded Deep Learning Framework for Segmentation of Nuclei in Digital Histology Images.

Accurate segmentation of nuclei is an essential step in analysis of digital histology images for diagnostic and prognostic applications. Despite recen...

A fully automatic deep learning algorithm to segment rectal Cancer on MR images: a multi-center study.

The aim of the study is to present and tune a fully automatic deep learning algorithm to segment colorectal cancers (CRC) on MR images, based on a U-N...

Bilateral Analysis Boosts the Performance of Mammography-based Deep Learning Models in Breast Cancer Risk Prediction.

Breast cancer is one of the leading causes of death among women. Early prediction of breast cancer can significantly improve the survival rates. Breas...

Deep learning methods for lesion detection on mammography images: a comparative analysis.

Automatic lesion segmentation in mammography images assists in the diagnosis of breast cancer, which is the most common type of cancer especially amon...

Deep Learning Methods for Detecting Side Effects of Cancer Chemotherapies Reported in a Remote Monitoring Web Application.

The objective of our work was to develop deep learning methods for extracting and normalizing patient-reported free-text side effects in a cancer chem...

Predictors of Toxicity Among Older Adults with Cancer.

An increasing number of cancer patients are of advanced age as the incidence of cancer increases with age. In this article, the clinical predictors of...

Novel Imaging Biomarkers to Assess Oncologic Treatment-Related Changes.

Cancer therapeutics cause various treatment-related changes that may impact patient follow-up and disease monitoring. Although atypical responses such...

Prediction of seroma after total mastectomy using an artificial neural network algorithm.

Seroma is a common complication after mastectomy. To the best of our knowledge, no prediction models have been developed for this. Henceforth, medical...

Risk of Developing Breast Reconstruction Complications: A Machine-Learning Nomogram for Individualized Risk Estimation with and without Postmastectomy Radiation Therapy.

BACKGROUND: The purpose of this study was to create a nomogram using machine learning models predicting risk of breast reconstruction complications wi...

De novo Prediction of Cell-Drug Sensitivities Using Deep Learning-based Graph Regularized Matrix Factorization.

Application of artificial intelligence (AI) in precision oncology typically involves predicting whether the cancer cells of a patient (previously unse...

Detection and Weak Segmentation of Masses in Gray-Scale Breast Mammogram Images Using Deep Learning.

PURPOSE: In this paper, we propose deep-learning methodology with which to enhance the mass differentiation performance of convolutional neural networ...

HER2-ResNet: A HER2 classification method based on deep residual network.

BACKGROUND: HER2 gene expression is one of the main reference indicators for breast cancer detection and treatment, and it is also an important target...

Low-dose CT noise reduction based on local total variation and improved wavelet residual CNN.

BACKGROUND: Low-dose computed tomography (LDCT) is an effective method for reducing radiation exposure. However, reducing radiation dose leads to cons...

Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics.

MOTIVATION: Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method fo...

Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach.

BACKGROUND: Cell-surface proteins have been widely used as diagnostic and prognostic markers in cancer research and as targets for the development of ...

Development and Validation of a Deep Learning CT Signature to Predict Survival and Chemotherapy Benefit in Gastric Cancer: A Multicenter, Retrospective Study.

OBJECTIVE: We aimed to develop a deep learning-based signature to predict prognosis and benefit from adjuvant chemotherapy using preoperative computed...

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HER2 is an important prognostic and predictive biomarker in breast cancer. Its detection makes it possible to define which patients will benefit from ...