ICAM-reg: Interpretable Classification and Regression with Feature
Attribution for Mapping Neurological Phenotypes in Individual Scans

ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans

3 March 2021

Mariana da Silva

Carole Sudre

Logan Z. J. Williams

Petru-Daniel Tudosiu

F. Alfaro-Almagro

Stephen M. Smith

Papers citing "ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans"

7 / 7 papers shown

Title
A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor Segmentation Andrea Failla Salvatore Citraro Xiaoxi He Yi-Lun Pan Yunpeng Cai MedIm 34 4 0 02 Aug 2024
Disentangling representations of retinal images with generative models Sarah Muller Lisa M. Koch Hendrik P. A. Lensch Philipp Berens MedIm 34 3 0 29 Feb 2024
NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants Chen Xue Fan Wang Yuanzhuo Zhu Hui Li Deyu Meng Dinggang Shen C. Lian 57 2 0 01 Jan 2023
Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligence H. Nguyen Michael Clement Boris Mansencal Pierrick Coupé MedIm 39 6 0 07 Jun 2022
Learning Disentangled Representations in the Imaging Domain Xiao Liu Pedro Sanchez Spyridon Thermos Alison Q. OÑeil Sotirios A. Tsaftaris OOD DRL 32 71 0 26 Aug 2021
Using StyleGAN for Visual Interpretability of Deep Learning Models on Medical Images K. Schutte O. Moindrot P. Hérent Jean-Baptiste Schiratti S. Jégou FAtt MedIm 44 61 0 19 Jan 2021
A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras S. Laine Timo Aila 321 10,391 0 12 Dec 2018