Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations

31 May 2018

Papers citing "Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations"

8 / 8 papers shown

Title
DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications Adam Ivankay Mattia Rigotti P. Frossard OOD MedIm 29 1 0 05 Jul 2023
Explainable AI for Bioinformatics: Methods, Tools, and Applications Md. Rezaul Karim Tanhim Islam Oya Beyan Christoph Lange Michael Cochez Dietrich-Rebholz Schuhmann Stefan Decker 29 68 0 25 Dec 2022
Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods Zohaib Salahuddin Henry C. Woodruff A. Chatterjee Philippe Lambin 24 303 0 01 Nov 2021
BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer Diagnosis in Breast Ultrasound Images Boyu Zhang Aleksandar Vakanski Min Xian 27 11 0 05 Oct 2021
Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic Dataset Erico Tjoa Cuntai Guan XAI FAtt 16 27 0 07 Sep 2020
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks Arash Rahnama A.-Yu Tseng FAtt AAML FaML 17 5 0 20 May 2020
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions F. Altaf Syed Mohammed Shamsul Islam Naveed Akhtar N. Janjua OOD 29 200 0 15 Feb 2019
A Survey on Deep Learning in Medical Image Analysis G. Litjens Thijs Kooi B. Bejnordi A. Setio F. Ciompi Mohsen Ghafoorian Jeroen van der Laak Bram van Ginneken C. I. Sánchez OOD 334 10,621 0 19 Feb 2017