A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
Abstract Background There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, which constrain their deployment in biomedica...
Main Authors: | Magdalena Wysocka, Oskar Wysocki, Marie Zufferey, Dónal Landers, André Freitas |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2023-05-01
|
Series: | BMC Bioinformatics |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12859-023-05262-8 |
Similar Items
-
Generating Real-Time Explanations for GNNs via Multiple Specialty Learners and Online Knowledge Distillation
by: Tien-Cuong Bui, et al.
Published: (2023-01-01) -
Improving Performance of the PRYSTINE Traffic Sign Classification by Using a Perturbation-Based Explainability Approach
by: Kaspars Sudars, et al.
Published: (2022-01-01) -
Neural Network Explainable AI Based on Paraconsistent Analysis: An Extension
by: Francisco S. Marcondes, et al.
Published: (2021-10-01) -
Feature Interpretation Using Generative Adversarial Networks (FIGAN): A Framework for Visualizing a CNN’s Learned Features
by: Kyle A. Hasenstab, et al.
Published: (2023-01-01) -
Interpreting the decisions of CNNs via influence functions
by: Aisha Aamir, et al.
Published: (2023-07-01)