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CheX-Nomaly: Segmenting Lung Abnormalities from Chest Radiographs using Machine Learning
We present CheX-nomaly: a binary localization U-net model that leverages transfer learning techniques with the incorporation of an innovative contrastive learning approach.
Automated Coronary Calcium Scoring using U-Net Models through Semi-supervised Learning on Non-Gated CT Scans
While nongated CT scans are more periodically taken, it is harder to detect calcification and is usually taken for a purpose other than locating calcification in arteries.
Emphasis on the Minimization of False Negatives or False Positives in Binary Classification
The minimization of specific cases in binary classification, such as false negatives or false positives, grows increasingly important as humans begin to implement more machine learning into current products.
A Novel Mask R-CNN Model to Segment Heterogeneous Brain Tumors through Image Subtraction
We propose using a method performed by radiologists called image segmentation and applying it to machine learning models to prove a better segmentation.
PneumoXttention: A CNN compensating for Human Fallibility when Detecting Pneumonia through CXR images with Attention
Automatic Chest Radiograph X-ray (CXR) interpretation by machines is an important research topic of Artificial Intelligence.
