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Found 13 papers, 0 papers with code
Mammo-CLIP: Leveraging Contrastive Language-Image Pre-training (CLIP) for Enhanced Breast Cancer Diagnosis with Multi-view Mammography
For framework evaluation, we assembled two datasets retrospectively.
Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms
For this purpose, we employ local Transformer blocks to separately learn patch relationships within four mammograms acquired from two-view (CC/MLO) of two-side (right/left) breasts.
Virtual Adversarial Training for Semi-supervised Breast Mass Classification
This study aims to develop a novel computer-aided diagnosis (CAD) scheme for mammographic breast mass classification using semi-supervised learning.
Recent advances and clinical applications of deep learning in medical image analysis
Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis.
Automatic Pulmonary Artery-Vein Separation in CT Images using Twin-Pipe Network and Topology Reconstruction
We propose a novel method for automatic separation of pulmonary arteries and veins from chest CT images.
Phase Separation of Polyelectrolytes: The Effect of Charge Regulation
Complex coacervation, known as the liquid-liquid phase separation of solutions with oppositely charged polyelectrolytes, has attracted substantial interest in recent years.
Soft Condensed Matter Statistical Mechanics
Coarse-to-fine Airway Segmentation Using Multi information Fusion Network and CNN-based Region Growing
To improve the performance of the segmentation result, the CNN-based region growing method is designed to focus on obtaining small branches.
Interpretative Computer-aided Lung Cancer Diagnosis: from Radiology Analysis to Malignancy Evaluation
In addition, explanations of CDAM features proved that the shape and density of nodule regions were two critical factors that influence a nodule to be inferred as malignant, which conforms with the diagnosis cognition of experienced radiologists.
Evaluation of company investment value based on machine learning
In this paper, company investment value evaluation models are established based on comprehensive company information.
Applying a random projection algorithm to optimize machine learning model for breast lesion classification
Then, support vector machine (SVM) models embedded with several feature dimensionality reduction methods are built to predict likelihood of lesions being malignant.
Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images
Methods: In this study, we explore a new approach to build an optimal ML model.
Improving performance of CNN to predict likelihood of COVID-19 using chest X-ray images with preprocessing algorithms
In order to address this challenge, we in this study develop and test a new computer-aided diagnosis (CAD) scheme.
SD-CNN: a Shallow-Deep CNN for Improved Breast Cancer Diagnosis
To evaluate the validity of our approach, we first develop a deep-CNN using 49 CEDM cases collected from Mayo Clinic to prove the contributions from recombined images for improved breast cancer diagnosis (0. 86 in accuracy using LE imaging vs. 0. 90 in accuracy using both LE and recombined imaging).
