agitter
commented
4 years ago Thanks for the suggestions @birajaghoshal. I'm adding the abstract and DOI link for this paper as well:
Multi-label classification in deep learning is a practical yet challenging task, because class overlaps in the feature space means that each instance is associated with multiple class labels. This requires a prediction of more than one class category for each input instance. To the best of our knowledge, this is the first deep learning study which quantifies uncertainty and model interpretability in multi-label classification; as well as applying it to the problem of recognising proteins expressed in cell types in testes based on immunohistochemically stained images. Multi-label classification is achieved by thresholding the class probabilities, with the optimal thresholds adaptively determined by a grid search scheme based on Matthews correlation coefficients. We adopt MC-Dropweights to approximate Bayesian Inference in multi-label classification to evaluate the usefulness of estimating uncertainty with predictive score to avoid overconfident, incorrect predictions in decision making. Our experimental results show that the MC-Dropweights visibly improve the performance to estimate uncertainty compared to state of the art approaches.
birajaghoshal
I suggest to include "Human Protein Atlas (HPA), which aims to map all the proteins in the human body. Research is focused on protein science, understanding the biology and functions of human proteins expressed in different organs, and the underlying mechanisms leading to cancer and other diseases."
Recently we published a paper "Estimating Uncertainty in Deep Learning for Reporting Confidence: An Application on Cell Type Prediction in Testes Based on Proteomics". Ref: https://link.springer.com/chapter/10.1007/978-3-030-44584-3_18