AIMC Topic: Image Processing, Computer-Assisted

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Qualitative and quantitative assessment of accelerated liver diffusion-weighted imaging using deep-learning reconstruction in oncologic patients.

BMC medical imaging
BACKGROUND: Deep-learning (DL) reconstructions could improve image quality and reduce acquisition time in diffusion-weighted imaging (DWI). This study assessed, qualitatively and quantitatively, DL-DWI in liver metastasis of colorectal cancer patient...

High-performance parallel multi-scale attention network with explainable AI for intelligent diagnosis of leaf diseases in agricultural systems.

Scientific reports
Detecting leaf diseases is crucial for ensuring crop health and boosting agricultural productivity. An advanced deep learning-based framework is introduced for cassava and groundnut leaf disease detection, incorporating a suite of innovative techniqu...

Human visual attention-inspired knowledge distillation underlying interpretable computational pathology.

Scientific reports
Computational pathology leverages advanced deep-learning techniques to analyze medical images with high resolution. However, a trade-off exists between model lightweight, interpretability, and task performance in such real-world scenarios. Knowledge ...

An autoencoder and vision transformer based interpretability analysis on the performance differences in automated staging of second and third molars.

Scientific reports
The practical adoption of deep learning in high-stakes forensic applications, such as dental age estimation, is often limited by the 'black box' nature of the models. This study introduces a framework designed to enhance both performance and transpar...

Dual-modality fusion for mango disease classification using dynamic attention based ensemble of leaf & fruit images.

Scientific reports
Mango is one of the most beloved fruits and plays an indispensable role in the agricultural economies of many tropical countries like Pakistan, India, and other Southeast Asian countries. Similar to other fruits, mango cultivation is also threatened ...

D2FLS-Net: Dual-Stage DEM-guided Fusion Transformer for landslide segmentation.

PloS one
Landslide segmentation from remote sensing imagery is crucial for rapid disaster assessment and risk mitigation. Owing to the pronounced heterogeneity of landslide scales and the subtle visual contrast between some landslide bodies and their backgrou...

SCADET: A detection framework for AI-generated artwork integrating dynamic frequency attention and contrastive spectral analysis.

PloS one
With the rapid development of generative AI technology, AI-generated images pose significant challenges for authenticity verification and originality validation. This paper proposes SCADET, a novel detection framework that integrates Dynamic Frequenc...

Light-field deep learning enables high-throughput, scattering-mitigated calcium imaging.

Proceedings of the National Academy of Sciences of the United States of America
Light-field microscopy (LFM) enables high-throughput functional imaging by scanlessly encoding entire volumes in single snapshots. However, LFM's computational burden and vulnerability to scattering limit its application to biological imaging. We pre...

Mobile phone-based plasmodium parasites stage detection from Giemsa stained blood smear by convolutional neural networks.

Parasitology research
Plasmodium vivax is a malaria parasite with a broad geographic distribution worldwide. The unique biological characteristics of P. vivax, such as early gametocytogenesis and its latent hypnozoite stage, make it more difficult to control compared to P...

NeuroAgeFusionNet an ensemble deep learning framework integrating CNN, transformers, and GNN for robust brain age estimation using MRI scans.

Scientific reports
Brain age prediction based on anatomical MRI scans, as an essentially new measure in neuroimaging and aging research, provides a crucial marker for the early diagnosis of neurodegenerative diseases, cognitive health appraisal, and biological age pred...