AIMC Topic: Image Interpretation, Computer-Assisted
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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...
GKE-TUNet: Geometry-Knowledge Embedded TransUNet Model for Retinal Vessel Segmentation Considering Anatomical Topology.
IEEE journal of biomedical and health informatics
Nov 6, 2024
Automated retinal vessel segmentation is crucial for computer-aided clinical diagnosis and retinopathy screening. However, deep learning faces challenges in extracting complex intertwined structures and subtle small vessels from densely vascularized ...
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...
Specificity-Aware Federated Learning With Dynamic Feature Fusion Network for Imbalanced Medical Image Classification.
IEEE journal of biomedical and health informatics
Nov 6, 2024
Recently, federated learning has become a powerful technique for medical image classification due to its ability to utilize datasets from multiple clinical clients while satisfying privacy constraints. However, there are still some obstacles in feder...
Minimizing prostate diffusion weighted MRI examination time through deep learning reconstruction.
Clinical imaging
Nov 5, 2024
PURPOSE: To study the diagnostic image quality of high b-value diffusion weighted images (DWI) derived from standard and variably reduced datasets reconstructed with a commercially available deep learning reconstruction (DLR) algorithm.
Multi-lesion segmentation guided deep attention network for automated detection of diabetic retinopathy.
Computers in biology and medicine
Nov 5, 2024
Accurate multi-lesion segmentation together with automated grading on fundus images played a vital role in diagnosing and treating diabetic retinopathy (DR). Nevertheless, the intrinsic patterns of fundus lesions aggravated challenges in DR detection...
Deep learning approaches for automated classification of neonatal lung ultrasound with assessment of human-to-AI interrater agreement.
Computers in biology and medicine
Nov 5, 2024
Neonatal respiratory disorders pose significant challenges in clinical settings, often requiring rapid and accurate diagnostic solutions for effective management. Lung ultrasound (LUS) has emerged as a promising tool to evaluate respiratory condition...
Scalable deep learning artificial intelligence histopathology slide analysis and validation.
Scientific reports
Nov 5, 2024
Deep learning involves an artificial intelligence (AI) approach and has been shown to provide superior performance for automating image recognition tasks, as well as exceeding human capabilities in both time and accuracy. Histopathology diagnostics i...
BrainMass: Advancing Brain Network Analysis for Diagnosis With Large-Scale Self-Supervised Learning.
IEEE transactions on medical imaging
Nov 4, 2024
Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-collect medical data, this approach is especially beneficial for medical ...
Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level Teacher.
IEEE transactions on medical imaging
Nov 4, 2024
Multiple Instance Learning (MIL) has demonstrated promise in Whole Slide Image (WSI) classification. However, a major challenge persists due to the high computational cost associated with processing these gigapixel images. Existing methods generally ...
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