Helping Breast Cancer Diagnosis on Mammographies using Convolutional Neural Networks
Garcia-Mojon, R.; Martin-Rodriguez, F.; Fernandez-Barciela, M.
Show abstract
In this paper a study about breast cancer detection is presented. Mammography images in DICOM format are processed using Convolutional Neural Networks (CNNs) to get a pre-diagnosis. Of course, this preliminary result needs to be checked by a trained radiologist. CNNs are trained and checked using a big database that is publicly available. Standard measurements for success are computed (accuracy, precision, recall) obtaining outstanding results better than other examples from the literature.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Model uncertainty estimates for deep learning mammographic density prediction using ordinal and classification approaches 94%
- Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality 90%
- Mammographic density assessed using deep learning in women at high risk of developing breast cancer: the effect of weight change on density 89%
Similar papers in this journal
- Estimation of Three-Dimensional Chromatin Morphology for Nuclear Classification and Characterisation 97%
- Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and Texture Descriptors 94%
- On evaluation metrics for medical applications of artificial intelligence 94%
Similar papers in this journal
- Classification and Visualisation of Normal and Abnormal Radiographs; a comparison between Eleven Convolutional Neural Network Architectures 96%
- A computational study on the role of parameters for identification of thyroid nodules by infrared images (and its comparison with real data) 94%
- Measuring Repositioning in Home Care for Pressure Injury Prevention and Management 93%
Similar papers in this journal
- DermoExpert: Skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning, and augmentation 95%
- An Inexpensive Smartphone-Based Device and Predictive Models for Rapid, Non-Invasive, and Point-of-Care Monitoring of Ocular and Cardiovascular Complications Related to Diabetes 94%
- TrajectoryViz: Interactive visualization of treatment trajectories 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.