AIMC Topic: Image Processing, Computer-Assisted

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SugarViT-Multi-objective regression of UAV images with Vision Transformers and Deep Label Distribution Learning demonstrated on disease severity prediction in sugar beet.

PloS one
Remote sensing and artificial intelligence are pivotal technologies of precision agriculture nowadays. The efficient retrieval of large-scale field imagery combined with machine learning techniques shows success in various tasks like phenotyping, wee...

Image captioning in Bengali language using visual attention.

PloS one
Automatically generating image captions poses one of the most challenging applications within artificial intelligence due to its integration of computer vision and natural language processing algorithms. This task becomes notably more formidable when...

ILR-Net: Low-light image enhancement network based on the combination of iterative learning mechanism and Retinex theory.

PloS one
Images captured in nighttime or low-light environments are often affected by external factors such as noise and lighting. Aiming at the existing image enhancement algorithms tend to overly focus on increasing brightness, while neglecting the enhancem...

Monitoring Immunohistochemical Staining Variations Using Artificial Intelligence on Standardized Controls.

Laboratory investigation; a journal of technical methods and pathology
Quality control of immunohistochemistry (IHC) slides is crucial to ascertain accurate patient management. Visual assessment is the most commonly used method to assess the quality of IHC slides from patient samples in daily pathology routines. Control...

FakET: Simulating cryo-electron tomograms with neural style transfer.

Structure (London, England : 1993)
In cryo-electron microscopy, accurate particle localization and classification are imperative. Recent deep learning solutions, though successful, require extensive training datasets. The protracted generation time of physics-based models, often emplo...

EAMAPG: Explainable Adversarial Model Analysis via Projected Gradient Descent.

Computers in biology and medicine
Despite the outstanding performance of deep learning (DL) models, their interpretability remains a challenging topic. In this study, we address the transparency of DL models in medical image analysis by introducing a novel interpretability method usi...

Gabor-modulated depth separable convolution for retinal vessel segmentation in fundus images.

Computers in biology and medicine
BACKGROUND: In diabetic retinopathy, precise segmentation of retinal vessels is essential for accurate diagnosis and effective disease management. This task is particularly challenging due to the varying sizes of vessels, their bifurcations, and the ...

Comparative analysis of machine learning models for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer: An MRI radiomics approach.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: The aim of this work is to compare different machine learning models for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer using radiomics features from dynamic contrast-enhanced magnetic reso...

Diffusion-driven multi-modality medical image fusion.

Medical & biological engineering & computing
Multi-modality medical image fusion (MMIF) technology utilizes the complementarity of different modalities to provide more comprehensive diagnostic insights for clinical practice. Existing deep learning-based methods often focus on extracting the pri...

Eliminating the second CT scan of dual-tracer total-body PET/CT via deep learning-based image synthesis and registration.

European journal of nuclear medicine and molecular imaging
PURPOSE: This study aims to develop and validate a deep learning framework designed to eliminate the second CT scan of dual-tracer total-body PET/CT imaging.