AIMC Topic: Image Processing, Computer-Assisted

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Image key information processing using convolutional neural network and rotational invariant-hierarchical max pooling algorithm.

PloS one
In the information age, the effectiveness of image processing determines the quality of a large number of image analysis tasks. A fusion algorithm-based processing technique was proposed to process key image information. A feature dictionary was intr...

Image guided construction of a common coordinate framework for spatial transcriptome data.

Scientific reports
Spatial transcriptomics is a powerful technology for high-resolution mapping of gene expression in tissue samples, enabling a molecular level understanding of tissue architecture. The acquisition entails dissecting and profiling micron-thick tissue s...

PDS-UKAN: Subdivision hopping connected to the U-KAN network for medical image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate and efficient segmentation of medical images plays a vital role in clinical tasks, such as diagnostic procedures and planning treatments. Traditional U-shaped encoder-decoder architectures, built on convolutional and transformer-based networ...

EnsembleEdgeFusion: advancing semantic segmentation in microvascular decompression imaging with innovative ensemble techniques.

Scientific reports
Semantic segmentation involves an imminent part in the investigation of medical images, particularly in the domain of microvascular decompression, where publicly available datasets are scarce, and expert annotation is demanding. In response to this c...

Cell-TRACTR: A transformer-based model for end-to-end segmentation and tracking of cells.

PLoS computational biology
Deep learning-based methods for identifying and tracking cells within microscopy images have revolutionized the speed and throughput of data analysis. These methods for analyzing biological and medical data have capitalized on advances from the broad...

Imputing single-cell protein abundance in multiplex tissue imaging.

Nature communications
Multiplex tissue imaging enables single-cell spatial proteomics and transcriptomics but remains limited by incomplete molecular profiling, tissue loss, and probe failure. Here, we apply machine learning to impute single-cell protein abundance using m...

EFCRFNet: A novel multi-scale framework for salient object detection.

PloS one
Salient Object Detection (SOD) is a fundamental task in computer vision, aiming to identify prominent regions within images. Traditional methods and deep learning-based models often encounter challenges in capturing crucial information in complex sce...

A Complete Transfer Learning-Based Pipeline for Discriminating Between Select Pathogenic Yeasts from Microscopy Photographs.

Pathogens (Basel, Switzerland)
Pathogenic yeasts are an increasing concern in healthcare, with species like often displaying drug resistance and causing high mortality in immunocompromised patients. The need for rapid and accessible diagnostic methods for accurate yeast identific...

A generative adversarial network-based accurate masked face recognition model using dual scale adaptive efficient attention network.

Scientific reports
Masked identification of faces is necessary for authentication purposes. Face masks are frequently utilized in a wide range of professions and sectors including public safety, health care, schooling, catering services, production, sales, and shipping...

SwinFishNet: A Swin Transformer-based approach for automatic fish species classification using transfer learning.

PloS one
The fish market is a crucial industry for both domestic economies and the global seafood trade. Accurate fish species classification (FSC) plays a significant role in ensuring sustainability, improving food safety, and optimizing market efficiency. T...