A Deep Learning Model for Breast Cancer Risk Quantification using Full-Field Digital Mammography and Breast Magnetic Resonance Images

By: Ritvik Pulya Every year, almost 30% of all breast cancer cases are diagnosed late, with a 68% difference in five-year survival rates between Stage 2 and Stage 4 individuals (American Cancer Society). However, current breast cancer risk estimation models such as the Tyler-Cuzick and Gail models have moderate predictive accuracy (only slightly better than 50% or choosing an outcome …

Electrocardiogram-Based Abnormal Heartbeat Classification: A Deep Learning Approach for Arrhythmia Detection

By: Aditya Kendre Heart arrhythmias are irregular rhythms in heartbeats that affect 3 million people worldwide every year. Due to the increasing rate of ECGs recording for diagnosis, it is now possible to devolve autonomous AI driven systems to identify arrhythmias in ECGs. A convolutional neural network (CNN) was developed and trained, to achieve accuracy on par or better than …