AIMC Topic: Frozen Sections
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Exploratory multi-cohort, multi-reader study on the clinical utility of a deep learning model for transforming cryosectioned to formalin-fixed, paraffin-embedded (FFPE) images in breast lesion diagnosis.
Breast cancer research : BCR
Jun 17, 2025
BACKGROUND: Cryosectioned tissues often exhibit artifacts that compromise pathologists' diagnostic accuracy during intraoperative assessments. These inconsistencies, compounded by variations in frozen section (FS) production across laboratories, high...
Fast and accurate lung cancer subtype classication and localization based on Intraoperative frozen sections of lung adenocarcinoma.
Biomedical physics & engineering express
Jun 16, 2025
Current lung cancer diagnostic techniques primarily focus on tissue subtype classification, yet remain inadequate in distinguishing pathological progression subtypes (particularly between adenocarcinomaand invasive adenocarcinoma) on frozen sections....
Deep learning-based analysis of gross features for ovarian epithelial tumors classification: A tool to assist pathologists for frozen section sampling.
Human pathology
Mar 22, 2025
Computational pathology has primarily focused on analyzing tissue slides, neglecting the valuable information contained in gross images. To bridge this gap, we proposed a novel approach leveraging the Swin Transformer architecture to develop a Swin-T...
Detection of metastatic breast carcinoma in sentinel lymph node frozen sections using an artificial intelligence-assisted system.
Pathology, research and practice
Feb 11, 2025
We developed an automatic method based on a convolutional neural network (CNN) that identifies metastatic lesions in whole slide images (WSI) of intraoperative frozen sections from sentinel lymph nodes in breast cancer. A total of 954 sentinel lymph ...
A Multi-Perspective Self-Supervised Generative Adversarial Network for FS to FFPE Stain Transfer.
IEEE transactions on medical imaging
Feb 4, 2025
In clinical practice, frozen section (FS) images can be utilized to obtain the immediate pathological results of the patients in operation due to their fast production speed. However, compared with the formalin-fixed and paraffin-embedded (FFPE) imag...
Deep Learning for Automated Segmentation of Basal Cell Carcinoma on Mohs Micrographic Surgery Frozen Section Slides.
Dermatologic surgery : official publication for American Society for Dermatologic Surgery [et al.]
Dec 3, 2024
BACKGROUND: Deep learning has been used to classify basal cell carcinoma (BCC) on histopathologic images. Segmentation models, required for localization of tumor on Mohs surgery (MMS) frozen section slides, have yet to reach clinical utility.
The Future of Surgical Diagnostics: Artificial Intelligence-Enhanced Detection of Ganglion Cells for Hirschsprung Disease.
Laboratory investigation; a journal of technical methods and pathology
Nov 20, 2024
Hirschsprung disease, a congenital disease characterized by the absence of ganglion cells, presents significant surgical challenges. Addressing a critical gap in intraoperative diagnostics, we introduce transformative artificial intelligence approach...
Pathology diagnosis of intraoperative frozen thyroid lesions assisted by deep learning.
BMC cancer
Aug 29, 2024
BACKGROUND: Thyroid cancer is a common thyroid malignancy. The majority of thyroid lesion needs intraoperative frozen pathology diagnosis, which provides important information for precision operation. As digital whole slide images (WSIs) develop, dee...
A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma.
Nature communications
May 4, 2024
Accurate intraoperative differentiation of primary central nervous system lymphoma (PCNSL) remains pivotal in guiding neurosurgical decisions. However, distinguishing PCNSL from other lesions, notably glioma, through frozen sections challenges pathol...
Improved artificial intelligence discrimination of minor histological populations by supplementing with color-adjusted images.
Scientific reports
Nov 4, 2023
Despite the dedicated research of artificial intelligence (AI) for pathological images, the construction of AI applicable to histopathological tissue subtypes, is limited by insufficient dataset collection owing to disease infrequency. Here, we prese...
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