AIMC Topic: Multimodal Imaging
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Enhanced breast cancer diagnosis through integration of computer vision with fusion based joint transfer learning using multi modality medical images.
Scientific reports
Nov 17, 2024
Breast cancer (BC) is a type of cancer which progresses and spreads from breast tissues and gradually exceeds the entire body; this kind of cancer originates in both sexes. Prompt recognition of this disorder is most significant in this phase, and it...
Precision of artificial intelligence in paediatric cardiology multimodal image interpretation.
Cardiology in the young
Nov 11, 2024
Multimodal imaging is crucial for diagnosis and treatment in paediatric cardiology. However, the proficiency of artificial intelligence chatbots, like ChatGPT-4, in interpreting these images has not been assessed. This cross-sectional study evaluates...
Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features.
PloS one
Nov 11, 2024
BACKGROUND AND PURPOSE: Glioblastoma is a highly aggressive brain tumor with limited survival that poses challenges in predicting patient outcomes. The Karnofsky Performance Status (KPS) score is a valuable tool for assessing patient functionality an...
An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer.
Nature communications
Nov 7, 2024
Multi-modal image analysis using deep learning (DL) lays the foundation for neoadjuvant treatment (NAT) response monitoring. However, existing methods prioritize extracting multi-modal features to enhance predictive performance, with limited consider...
Machine-learning based prediction of future outcome using multimodal MRI during early childhood.
Seminars in fetal & neonatal medicine
Nov 7, 2024
The human brain undergoes rapid changes from the fetal stage to two years postnatally, during which proper structural and functional maturation lays the foundation for later cognitive and behavioral development. Multimodal magnetic resonance imaging ...
Framework for Deep Learning Based Multi-Modality Image Registration of Snapshot and Pathology Images.
IEEE journal of biomedical and health informatics
Nov 6, 2024
Multi-modality image registration is an important task in medical imaging because it allows for information from different domains to be correlated. Histopathology plays a crucial role in oncologic surgery as it is the gold standard for investigating...
Multi-Loss Disentangled Generative-Discriminative Learning for Multimodal Representation in Schizophrenia.
IEEE journal of biomedical and health informatics
Nov 6, 2024
Schizophrenia (SCZ) is a multifactorial mental illness, thus it will be beneficial for exploring this disease using multimodal data, including functional magnetic resonance imaging (fMRI), genes, and the gut microbiome. Previous studies reported comb...
Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions.
Scientific reports
Nov 5, 2024
Self-supervised learning has become the cornerstone of building generalizable and transferable artificial intelligence systems in medical imaging. In particular, contrastive representation learning techniques trained on large multi-modal datasets hav...
Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction.
IEEE transactions on medical imaging
Nov 4, 2024
Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis in clinical medicine. However, acquiring certain modalities, such as T2-weighted images (T2WIs), is time-consuming and prone to be with motion artifa...
A Stacked Multimodality Model Based on Functional MRI Features and Deep Learning Radiomics for Predicting the Early Response to Radiotherapy in Nasopharyngeal Carcinoma.
Academic radiology
Nov 3, 2024
BACKGROUND: This study aimed to construct and assess a comprehensive model that integrates MRI-derived deep learning radiomics, functional imaging (fMRI), and clinical indicators to predict early efficacy of radiotherapy in nasopharyngeal carcinoma (...
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