Medical Datasets

Medical datasets are crucial for training effective machine learning models in healthcare, but their inherent privacy concerns and heterogeneity pose significant challenges. Current research focuses on developing methods to mitigate these issues, including techniques for synthesizing private-preserving datasets, distilling large datasets into smaller, representative subsets, and employing federated learning to train models collaboratively across multiple institutions without directly sharing sensitive data. These advancements are vital for improving the accuracy and generalizability of AI models in healthcare, ultimately leading to better diagnostics, treatment, and resource allocation.

Papers

October 24, 2022

Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar
Tabular Data Model Generalization Medical Datasets Heterogeneous Effect Subgroup Description

October 4, 2022

CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification
Suraj Kothawade, Atharv Savarkar, Venkat Iyer, Lakshman Tamil, Ganesh Ramakrishnan, Rishabh Iyer
Active Learning Medical Image Datasets Medical Datasets Clinical Language Imbalanced Medical Image

October 3, 2022

Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets
Sidra Aleem, Teerath Kumar, Suzanne Little, Malika Bendechache, Rob Brennan, Kevin McGuinness
Medical Image Analysis Feature Enhancement Medical Datasets Enhancement Model Random Data Augmentation Based Enhancement Model

September 29, 2022

Dataset Distillation for Medical Dataset Sharing
Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama
Dataset Distillation Medical Datasets COVID 19 Chest X Ray Medical Data Sharing xApp Distillation

September 16, 2022

De-Identification of French Unstructured Clinical Notes for Machine Learning Tasks
Yakini Tchouka, Jean-François Couchot, Maxime Coulmeau, David Laiymani, Philippe Selles, Azzedine Rahmani
Clinical Note Medical Datasets Machine Learning Task De Identification French Clinical

September 13, 2022

Investigating the Predictive Reproducibility of Federated Graph Neural Networks using Medical Datasets
Mehmet Yigit Balik, Arwa Rekik, Islem Rekik
Medical Datasets List Replicability Federated Graph Neural Network

September 8, 2022

FedDAR: Federated Domain-Aware Representation Learning
Aoxiao Zhong, Hao He, Zhaolin Ren, Na Li, Quanzheng Li
Federated Learning Heterogeneous Data Federated Prompt Cooperation Medical Datasets Cross Silo Federated Learning Personalized Prediction

August 31, 2022

Non-readily identifiable data collaboration analysis for multiple datasets including personal information
Akira Imakura, Tetsuya Sakurai, Yukihiko Okada, Tomoya Fujii, Teppei Sakamoto, Hiroyuki Abe
Private Data Medical Datasets Personal Information Data Fusion Data Collaboration

August 18, 2022

Outlier Detection using Self-Organizing Maps for Automated Blood Cell Analysis
Stefan Röhrl, Alice Hein, Lucie Huang, Dominik Heim, Christian Klenk, Manuel Lengl, Martin Knopp, Nawal Hafez, Oliver Hayden, Klaus Diepold
Outlier Detection Medical Datasets Global Outlier Self Organizing Map

August 10, 2022

PatchDropout: Economizing Vision Transformers Using Patch Dropout
Yue Liu, Christos Matsoukas, Fredrik Strand, Hossein Azizpour, Kevin Smith
Vision Transformer Supervised ImageNet Natural Image Medical Datasets Image Patch

July 23, 2022

Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data
Zepeng Huo, Xiaoning Qian, Shuai Huang, Zhangyang Wang, Bobak J. Mortazavi
Risk Prediction Training Model Medical Datasets Aware Loss Imbalanced Medical Class Density

July 14, 2022

DEXTER: An end-to-end system to extract table contents from electronic medical health documents
Nandhinee PR, Harinath Krishnamoorthy, Koushik Srivatsan, Anil Goyal, Sudarsun Santhiappan
End to End Electronic Health Record Medical Datasets Table Detection Cell Recognition

July 7, 2022

Towards the Practical Utility of Federated Learning in the Medical Domain
Seongjun Yang, Hyeonji Hwang, Daeyoung Kim, Radhika Dua, Jong-Yeup Kim, Eunho Yang, Edward Choi
Data Heterogeneity Task Utility Medical Domain Medical Datasets

June 27, 2022

Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance
Meirui Jiang, Hongzheng Yang, Xiaoxiao Li, Quande Liu, Pheng-Ann Heng, Qi Dou
Semi Supervised Class Imbalance Medical Datasets Dynamic Learning Federated Image Classification

June 20, 2022

Autoencoder-based Attribute Noise Handling Method for Medical Data
Thomas Ranvier, Haytham Elgazel, Emmanuel Coquery, Khalid Benabdeslem
Pre Training Tabular Data Imputation Method Medical Datasets Medical Data Denoising Autoencoder Feature Noise

June 4, 2022

Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets
Sreejita Ghosh, Elizabeth S. Baranowski, Michael Biehl, Wiebke Arlt, Peter Tino, Kerstin Bunte
Class Imbalance Interpretable Model Patient Data Medical Datasets Heterogeneous Datasets Missingness Pattern

June 2, 2022

Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation
Virginia Adams, Sandeep Subramanian, Mike Chrzanowski, Oleksii Hrinchuk, Oleksii Kuchaiev
Domain Adaptation Machine Translation Neural Machine Translation Complete Recipe Medical Datasets

April 16, 2022

Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case
Febrianti Wibawa, Ferhat Ozgur Catak, Salih Sarp, Murat Kuzlu, Umit Cali
Convolutional Neural Network Deep Learning Model Homomorphic Encryption COVID 19 Detection Medical Datasets

April 6, 2022

OSCARS: An Outlier-Sensitive Content-Based Radiography Retrieval System
Xiaoyuan Guo, Jiali Duan, Saptarshi Purkayastha, Hari Trivedi, Judy Wawira Gichoya, Imon Banerjee
Medical Datasets Intra Class Variation Medical Image Retrieval

April 4, 2022

Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models
Ashkan Khakzar, Yawei Li, Yang Zhang, Mirac Sanisoglu, Seong Tae Kim, Mina Rezaei, Bernd Bischl, Nassir Navab
Convolutional Neural Network Full Model Medical Image Mixed Effect Data Imbalance Medical Datasets Group Imbalance Imbalanced Dataset Learned Feature

Medical Datasets

Papers

Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data

CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification

Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets

Dataset Distillation for Medical Dataset Sharing

De-Identification of French Unstructured Clinical Notes for Machine Learning Tasks

Investigating the Predictive Reproducibility of Federated Graph Neural Networks using Medical Datasets

FedDAR: Federated Domain-Aware Representation Learning

Non-readily identifiable data collaboration analysis for multiple datasets including personal information

Outlier Detection using Self-Organizing Maps for Automated Blood Cell Analysis

PatchDropout: Economizing Vision Transformers Using Patch Dropout

Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data

DEXTER: An end-to-end system to extract table contents from electronic medical health documents

Towards the Practical Utility of Federated Learning in the Medical Domain

Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance

Autoencoder-based Attribute Noise Handling Method for Medical Data

Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets

Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation

Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case

OSCARS: An Outlier-Sensitive Content-Based Radiography Retrieval System

Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models