Medical Imaging

Medical imaging research focuses on developing and improving AI-powered methods for analyzing medical images, primarily aiming to enhance diagnostic accuracy, efficiency, and accessibility. Current research emphasizes robust model architectures (like Vision Transformers and UNets) and algorithms (including federated learning, generative adversarial networks, and diffusion models) to address challenges such as data scarcity, domain shifts (e.g., scanner variations), and privacy concerns. These advancements hold significant potential for improving clinical decision-making, particularly in areas with limited radiologist access, and for facilitating more efficient and reliable medical diagnoses.

Papers

February 2, 2022

Posterior temperature optimized Bayesian models for inverse problems in medical imaging
Max-Heinrich Laves, Malte Tölle, Alexander Schlaefer, Sandy Engelhardt
Inverse Problem Medical Imaging Mean Field Bayesian Model Cold Posterior

January 24, 2022

Transformers in Medical Imaging: A Survey
Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat, Fahad Shahbaz Khan, Huazhu Fu
Convolutional Neural Network Timely Survey Computer Vision Transformer Megatron Decepticons Medical Image Segmentation Transformer Model Medical Imaging Transformer Based Medical

January 21, 2022

The Security of Deep Learning Defences for Medical Imaging
Moshe Levy, Guy Amit, Yuval Elovici, Yisroel Mirsky
Deep Learning Deep Neural Network Deep Learning Model Medical Imaging Adversarial Sample Security Related

January 19, 2022

A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis
Muhammad Muneeb Saad, Ruairi O'Reilly, Mubashir Husain Rehmani
Timely Survey Generative Adversarial Network Medical Imaging Biomedical Image Synthetic Medical Image Biomedical Image Analysis

December 22, 2021

Fusion of medical imaging and electronic health records with attention and multi-head machanisms
Cheng Jiang, Yihao Chen, Jianbo Chang, Ming Feng, Renzhi Wang, Jianhua Yao
Medical Image Human Attention Medical Imaging Electronic Health Record Hybrid Fusion Cross Modal Attention Multi Head Multimodal Machine

December 18, 2021

Cross-Domain Federated Learning in Medical Imaging
Vishwa S Parekh, Shuhao Lai, Vladimir Braverman, Jeff Leal, Steven Rowe, Jay J Pillai, Michael A Jacobs
Federated Learning Cross Domain Medical Imaging Multi Domain

December 6, 2021

Organ localisation using supervised and semi supervised approaches combining reinforcement learning with imitation learning
Sankaran Iyer, Alan Blair, Laughlin Dawes, Daniel Moses, Christopher White, Arcot Sowmya
Reinforcement Learning Semi Supervised Learning Imitation Learning Semi Supervised Constructive Approach Medical Imaging CT Image Multi Organ

December 2, 2021

FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis
Yu Feng, Benteng Ma, Jing Zhang, Shanshan Zhao, Yong Xia, Dacheng Tao
Medical Image Backdoor Attack Unified Framework Medical Image Analysis Medical Imaging

November 29, 2021

Enhanced Transfer Learning Through Medical Imaging and Patient Demographic Data Fusion
Spencer A. Thomas
Supervised ImageNet Medical Imaging Medical Image Data Imaging Data Non Image Data

November 26, 2021

ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics
Aiham Taleb, Matthias Kirchler, Remo Monti, Christoph Lippert
Contrastive Learning Medical Image Contrastive Loss Cross Modal Medical Imaging Microbial Genome State of the Art Self Multimodal Self Supervised Learning

November 23, 2021

RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR
Yuyin Zhou, Shih-Cheng Huang, Jason Alan Fries, Alaa Youssef, Timothy J. Amrhein, Marcello Chang, Imon Banerjee, Daniel Rubin, Lei Xing, Nigam Shah, Matthew P. Lungren
Procedural Fairness Medical Imaging Electronic Health Record Benchmark Dataset Multi Modality Imaging Electronic Health Record Data Multimodal Fusion Model Pulmonary Embolism

November 16, 2021

Two-step adversarial debiasing with partial learning -- medical image case-studies
Ramon Correa, Jiwoong Jason Jeong, Bhavik Patel, Hari Trivedi, Judy W. Gichoya, Imon Banerjee
Medical Imaging Implicit Bias AI Model Adversarial Debiasing Bias Reduction Two Step

November 15, 2021

Solving Inverse Problems in Medical Imaging with Score-Based Generative Models
Yang Song, Liyue Shen, Lei Xing, Stefano Ermon
Inverse Problem Medical Imaging Score Based Generative Unsupervised Method Measurement Operator Incomplete Measurement

November 12, 2021

Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2
Matthias Griebel, Dennis Segebarth, Nikolai Stein, Nina Schukraft, Philip Tovote, Robert Blum, Christoph M. Flath
Deep Learning Instance Segmentation Medical Imaging Reliable Segmentation Ambiguous Data

Medical Imaging

Papers

Posterior temperature optimized Bayesian models for inverse problems in medical imaging

Transformers in Medical Imaging: A Survey

The Security of Deep Learning Defences for Medical Imaging

A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis

Fusion of medical imaging and electronic health records with attention and multi-head machanisms

Cross-Domain Federated Learning in Medical Imaging

Organ localisation using supervised and semi supervised approaches combining reinforcement learning with imitation learning

FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis

Enhanced Transfer Learning Through Medical Imaging and Patient Demographic Data Fusion

ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics

RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR

Two-step adversarial debiasing with partial learning -- medical image case-studies

Solving Inverse Problems in Medical Imaging with Score-Based Generative Models

Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2