Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications

20 March 2018

Papers citing "Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications"

4 / 4 papers shown

Title
Measure and Improve Robustness in NLP Models: A Survey Xuezhi Wang Haohan Wang Diyi Yang 139 130 0 15 Dec 2021
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography A. Barnett F. Schwartz Chaofan Tao Chaofan Chen Yinhao Ren J. Lo Cynthia Rudin 31 133 0 23 Mar 2021
On the Origin of Deep Learning Haohan Wang Bhiksha Raj MedIm 3DV VLM 40 223 0 24 Feb 2017
Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound Fausto Milletari Seyed-Ahmad Ahmadi Christine Kroll A. Plate Verena E. Rozanski ... J. Levin O. Dietrich B. Ertl-Wagner K. Boetzel Nassir Navab 200 363 0 26 Jan 2016