Search Results for author:
Found 5 papers, 0 papers with code
COIN: Contrastive Identifier Network for Breast Mass Diagnosis in Mammography
Computer-aided breast cancer diagnosis in mammography is a challenging problem, stemming from mammographical data scarcity and data entanglement.
Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography
In this paper, we introduce a novel deep learning framework for mammogram image processing, which computes mass segmentation and simultaneously predict diagnosis results.
Fast and automated biomarker detection in breath samples with machine learning
Volatile organic compounds (VOCs) in human breath can reveal a large spectrum of health conditions and can be used for fast, accurate and non-invasive diagnostics.
Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis
Computer-aided breast cancer diagnosis in mammography is limited by inadequate data and the similarity between benign and cancerous masses.
A Deep DUAL-PATH Network for Improved Mammogram Image Processing
We present, for the first time, a novel deep neural network architecture called \dcn with a dual-path connection between the input image and output class label for mammogram image processing.
