AIMC Topic: Image Processing, Computer-Assisted

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Deep learning to capture leaf shape in plant images: Validation by geometric morphometrics.

The Plant journal : for cell and molecular biology
Plant leaves play a pivotal role in automated species identification using deep learning (DL). However, achieving reproducible capture of leaf variation remains challenging due to the inherent "black box" problem of DL models. To evaluate the effecti...

Deep Spatio-Temporal Network for Low-SNR Cryo-EM Movie Frame Enhancement.

IEEE/ACM transactions on computational biology and bioinformatics
Cryo-EM in single particle analysis is known to have low SNR and requires to utilize several frames of the same particle sample to restore one high-quality image for visualizing that particle. However, the low SNR of cryo-EM movie and motion caused b...

Enhancing dental caries classification in CBCT images by using image processing and self-supervised learning.

Computers in biology and medicine
Diagnosing dental caries poses a significant challenge in dentistry, necessitating precise and early detection for effective management. This study utilizes Self-Supervised Learning (SSL) tasks to improve the classification of dental caries in Cone B...

A review of deep learning approaches for multimodal image segmentation of liver cancer.

Journal of applied clinical medical physics
This review examines the recent developments in deep learning (DL) techniques applied to multimodal fusion image segmentation for liver cancer. Hepatocellular carcinoma is a highly dangerous malignant tumor that requires accurate image segmentation f...

Comparing Human-Level and Machine Learning Model Performance in White Blood Cell Morphology Assessment.

European journal of haematology
INTRODUCTION: There is an increasing research focus on the role of machine learning in the haematology laboratory, particularly in blood cell morphologic assessment. Human-level performance is an important baseline and goal for machine learning. This...

Ranking attention multiple instance learning for lymph node metastasis prediction on multicenter cervical cancer MRI.

Journal of applied clinical medical physics
PURPOSE: In the current clinical diagnostic process, the gold standard for lymph node metastasis (LNM) diagnosis is histopathological examination following surgical lymphadenectomy. Developing a non-invasive and preoperative method for predicting LNM...

Distributional uniformity quantification in heterogeneous prepared dishes combined the hyperspectral imaging technology with Moran's I: A case study of pizza.

Food chemistry
Quality detection is critical in the development of prepared dishes, with distributional uniformity playing a significant role. This study used hyperspectral imaging (HSI) and Moran's I to quantify distributional uniformity, employing pizza as case. ...

MixUNETR: A U-shaped network based on W-MSA and depth-wise convolution with channel and spatial interactions for zonal prostate segmentation in MRI.

Neural networks : the official journal of the International Neural Network Society
Magnetic resonance imaging (MRI) plays a pivotal role in diagnosing and staging prostate cancer. Precise delineation of the peripheral zone (PZ) and transition zone (TZ) within prostate MRI is essential for accurate diagnosis and subsequent artificia...

A survey on cell nuclei instance segmentation and classification: Leveraging context and attention.

Medical image analysis
Nuclear-derived morphological features and biomarkers provide relevant insights regarding the tumour microenvironment, while also allowing diagnosis and prognosis in specific cancer types. However, manually annotating nuclei from the gigapixel Haemat...

Total-Body PET/CT: A Role of Artificial Intelligence?

Seminars in nuclear medicine
The purpose of this paper is to provide an overview of the cutting-edge applications of artificial intelligence (AI) technology in total-body positron emission tomography/computed tomography (PET/CT) scanning technology and its profound impact on the...