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

October 10, 2023

Exploring adversarial attacks in federated learning for medical imaging
Erfan Darzi, Florian Dubost, N. M. Sijtsema, P. M. A van Ooijen
LeArning Abstract Adversarial Attack Federated Learning Medical Imaging Pathology Datasets

October 4, 2023

Comprehensive Multimodal Segmentation in Medical Imaging: Combining YOLOv8 with SAM and HQ-SAM Models
Sumit Pandey, Kuan-Fu Chen, Erik B. Dam
Medical Image Segmentation Segment Anything Model Medical Image Analysis Medical Imaging Medical Image Datasets Segmentation Result Look Once Version 8 Boundary Detection Multimodal Segmentation

October 3, 2023

AI-based association analysis for medical imaging using latent-space geometric confounder correction
Xianjing Liu, Bo Li, Meike W. Vernooij, Eppo B. Wolvius, Gennady V. Roshchupkin, Esther E. Bron
Medical Image Analysis Medical Imaging Unobserved Confounders Latent Confounders Longitudinal Image Association Test

October 2, 2023

A Framework for Interpretability in Machine Learning for Medical Imaging
Alan Q. Wang, Batuhan K. Karaman, Heejong Kim, Jacob Rosenthal, Rachit Saluja, Sean I. Young, Mert R. Sabuncu
Machine Learning Machine Learning Model New Framework Inherent Interpretability Medical Image Analysis Medical Imaging Interpretability Research Model Transparency

September 27, 2023

Towards Foundation Models Learned from Anatomy in Medical Imaging via Self-Supervision
Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway, Jianming Liang
Foundation Model Medical Imaging Segmentation Task Self Supervision Supervised Baseline Anatomical Structure

September 22, 2023

Understanding Calibration of Deep Neural Networks for Medical Image Classification
Abhishek Singh Sambyal, Usma Niyaz, Narayanan C. Krishnan, Deepti R. Bathula
Deep Neural Network Medical Imaging Medical Image Classification Medical Image Datasets Model Calibration Calibration Metric

September 20, 2023

A Systematic Review of Few-Shot Learning in Medical Imaging
Eva Pachetti, Sara Colantonio
LeArning Abstract Medical Image Shot Learning Medical Imaging Systematic Review

September 4, 2023

August 25, 2023

ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis
Yanyan Huang, Weiqin Zhao, Shujun Wang, Yu Fu, Yuming Jiang, Lequan Yu
Continual LEArning Medical Imaging Whole Slide Image COntinual Learning Framework Interaction Transformer Breakup Reorganize Rehearsal

August 12, 2023

Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging
Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Michael Götz, Timo Ropinski
Deep Learning Large Scale Medical Imaging Masked Autoencoders Self Supervised Pre Training Small Datasets Medical Image Classification Task Self Supervised Pre Training Method

August 8, 2023

Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging
Sebastian Nørgaard Llambias, Mads Nielsen, Mostafa Mehdipour Ghazi
Domain Adaptation Data Augmentation Raw Data Domain Shift Medical Imaging Unsupervised Method Brain Segmentation Robust Domain Adaptation

July 31, 2023

July 30, 2023

Implicit Neural Representation in Medical Imaging: A Comparative Survey
Amirali Molaei, Amirhossein Aminimehr, Armin Tavakoli, Amirhossein Kazerouni, Bobby Azad, Reza Azad, Dorit Merhof
Medical Image Analysis Medical Imaging Implicit Neural Representation Medical Image Data Multi Modality Imaging

July 27, 2023

July 26, 2023

Role of Image Acquisition and Patient Phenotype Variations in Automatic Segmentation Model Generalization
Timothy L. Kline, Sumana Ramanathan, Harrison C. Gottlich, Panagiotis Korfiatis, Adriana V. Gregory
Integral Role Segmentation Model Medical Imaging Non Contrast Contrast Enhanced Patient Phenotype Image Acquisition

Medical Imaging

Papers

Exploring adversarial attacks in federated learning for medical imaging

Comprehensive Multimodal Segmentation in Medical Imaging: Combining YOLOv8 with SAM and HQ-SAM Models

AI-based association analysis for medical imaging using latent-space geometric confounder correction

A Framework for Interpretability in Machine Learning for Medical Imaging

Towards Foundation Models Learned from Anatomy in Medical Imaging via Self-Supervision

Understanding Calibration of Deep Neural Networks for Medical Image Classification

A Systematic Review of Few-Shot Learning in Medical Imaging

An Empirical Analysis for Zero-Shot Multi-Label Classification on COVID-19 CT Scans and Uncurated Reports

On the use of Mahalanobis distance for out-of-distribution detection with neural networks for medical imaging

Towards frugal unsupervised detection of subtle abnormalities in medical imaging

ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis

Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging

Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging

Federated Learning for Data and Model Heterogeneity in Medical Imaging

Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging

No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging

Implicit Neural Representation in Medical Imaging: A Comparative Survey

Generative AI for Medical Imaging: extending the MONAI Framework

Taxonomy Adaptive Cross-Domain Adaptation in Medical Imaging via Optimization Trajectory Distillation

Role of Image Acquisition and Patient Phenotype Variations in Automatic Segmentation Model Generalization