Non Image Data

Non-image data, encompassing diverse information like patient demographics, clinical records, and drug properties, is increasingly integrated with image data to improve the accuracy and efficiency of various machine learning applications. Current research focuses on developing multi-modal fusion techniques, often employing transformer networks and collaborative filtering frameworks, to effectively combine these disparate data types. This integration enhances model performance in diverse fields, including medical diagnosis (e.g., Alzheimer's disease, cancer), drug safety prediction, and dynamical systems modeling, leading to more robust and informative analyses. The ability to leverage non-image data promises significant advancements in personalized medicine, improved healthcare outcomes, and more accurate scientific modeling.

Papers

October 30, 2024

Rule by Rule: Learning with Confidence through Vocabulary Expansion
Albert Nössig, Tobias Hell, Georg Moser
High Confidence Simple RULE Text Data Rule Learning Iterative Approach Vocabulary Expansion Non Image Data

October 21, 2024

Deep Active Learning with Manifold-preserving Trajectory Sampling
Yingrui Ji, Vijaya Sindhoori Kaza, Nishanth Artham, Tianyang Wang
Active Learning Unlabeled Data Annotation Rather Deep Active Learning Trajectory Sampling Labeling Effort Non Image Data

November 18, 2023

Radiology Report Generation Using Transformers Conditioned with Non-imaging Data
Nurbanu Aksoy, Nishant Ravikumar, Alejandro F Frangi
Transformer Megatron Decepticons Medical Image Radiology Report Generation Radiology Report Medical Image Representation Non Image Data

August 3, 2023

ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction
Haoxuan Li, Taojun Hu, Zetong Xiong, Chunyuan Zheng, Fuli Feng, Xiangnan He, Xiao-Hua Zhou
Multi Label Collaborative Filtering Adverse Drug Drug Representation Non Image Data

June 26, 2023

TaylorPDENet: Learning PDEs from non-grid Data
Paul Heinisch, Andrzej Dulny, Anna Krause, Andreas Hotho
Dynamical System Complex Dynamical System Taylor Expansion Non Image Data Spatial Gradient

May 26, 2023

Large language models improve Alzheimer's disease diagnosis using multi-modality data
Yingjie Feng, Jun Wang, Xianfeng Gu, Xiaoyin Xu, Min Zhang
Large Language Model Pre Trained Language Model Alzheimer'S Disease Artificial Intelligence Model Multi Modal Data ADNI Dataset Non Image Data

April 18, 2022

Enhancing Non-mass Breast Ultrasound Cancer Classification With Knowledge Transfer
Yangrun Hu, Yuanfan Guo, Fan Zhang, Mingda Wang, Tiancheng Lin, Rong Wu, Yi Xu
Transfer Learning Knowledge Transfer Breast Ultrasound Transfer Learning Framework Lesion Image Non Image Data

March 25, 2022

Deep Multi-modal Fusion of Image and Non-image Data in Disease Diagnosis and Prognosis: A Review
Can Cui, Haichun Yang, Yaohong Wang, Shilin Zhao, Zuhayr Asad, Lori A. Coburn, Keith T. Wilson, Bennett A. Landman, Yuankai Huo
Multimodal Information Multimodal Fusion Multi Modal Fusion Multi Modal Learning Prognosis Prediction Clinical Decision Support System Multi Modal Deep Learning Non Image Data

February 7, 2022

Robust Dialogue State Tracking with Weak Supervision and Sparse Data
Michael Heck, Nurul Lubis, Carel van Niekerk, Shutong Feng, Christian Geishauser, Hsien-Chin Lin, Milica Gašić
Weak Supervision Dialogue State Tracking Sparse Data Grained Supervision Non Image Data Inference Based Adaptive Dropout

December 15, 2021

Know Thy Strengths: Comprehensive Dialogue State Tracking Diagnostics
Hyundong Cho, Chinnadhurai Sankar, Christopher Lin, Kaushik Ram Sadagopan, Shahin Shayandeh, Asli Celikyilmaz, Jonathan May, Ahmad Beirami
Transfer Learning Distribution Shift Real Power Dialogue State Tracking Span Classification Non Image Data

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

Non Image Data

Papers

Rule by Rule: Learning with Confidence through Vocabulary Expansion

Deep Active Learning with Manifold-preserving Trajectory Sampling

Radiology Report Generation Using Transformers Conditioned with Non-imaging Data

ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction

TaylorPDENet: Learning PDEs from non-grid Data

Large language models improve Alzheimer's disease diagnosis using multi-modality data

Enhancing Non-mass Breast Ultrasound Cancer Classification With Knowledge Transfer

Deep Multi-modal Fusion of Image and Non-image Data in Disease Diagnosis and Prognosis: A Review

Robust Dialogue State Tracking with Weak Supervision and Sparse Data

Know Thy Strengths: Comprehensive Dialogue State Tracking Diagnostics

Enhanced Transfer Learning Through Medical Imaging and Patient Demographic Data Fusion