Nuclei and glands instance segmentation in histology images: a narrative review
Examination of tissue biopsy and quantification of the various characteristics of cellular
processes are clinical benchmarks in cancer diagnosis. Nuclei and glands instance …
processes are clinical benchmarks in cancer diagnosis. Nuclei and glands instance …
Image analysis of nuclei histopathology using deep learning: A review of segmentation, detection, and classification
M Kadaskar, N Patil - SN Computer Science, 2023 - Springer
Deep learning has recently advanced in its applicability to computer vision challenges, and
medical imaging has become the most used technique in histopathology image analysis …
medical imaging has become the most used technique in histopathology image analysis …
Nuclei probability and centroid map network for nuclei instance segmentation in histology images
Nuclei instance segmentation is an integral step in digital pathology workflow as it is a
prerequisite for most downstream tasks such as patient survival analysis, precision …
prerequisite for most downstream tasks such as patient survival analysis, precision …
A scale and region-enhanced decoding network for nuclei classification in histology image
S Xiao, A Qu, H Zhong, P He - Biomedical Signal Processing and Control, 2023 - Elsevier
Accurate classification of nuclei in histology images is essential for clinical diagnosis,
prognosis, and therapeutic response prediction of cancer. However, this is still a challenging …
prognosis, and therapeutic response prediction of cancer. However, this is still a challenging …
MMSRNet: Pathological image super-resolution by multi-task and multi-scale learning
X Wu, Z Chen, C Peng, X Ye - Biomedical Signal Processing and Control, 2023 - Elsevier
Pathological diagnosis is the gold standard for disease assessment in clinical practice. It is
conducted by inspecting the specimen at the microscopical level. Therefore, a very high …
conducted by inspecting the specimen at the microscopical level. Therefore, a very high …
[HTML][HTML] Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain …
With the advent of digital pathology and microscopic systems that can scan and save whole
slide histological images automatically, there is a growing trend to use computerized …
slide histological images automatically, there is a growing trend to use computerized …
Simultaneously segmenting and classifying cell nuclei by using multi-task learning in multiplex immunohistochemical tissue microarray sections
R Wang, Y Qiu, X Hao, S Jin, J Gao, H Qi, Q Xu… - … Signal Processing and …, 2024 - Elsevier
Quantitative analysis of tumor immune microenvironment (TIME) in immunohistochemical
(IHC) tissue microarray (TMA) sections is crucial in diagnosis and treatment …
(IHC) tissue microarray (TMA) sections is crucial in diagnosis and treatment …
Learn from orientation prior for radiograph super-resolution: Orientation operator transformer
Background and objective: High-resolution radiographic images play a pivotal role in the
early diagnosis and treatment of skeletal muscle-related diseases. It is promising to enhance …
early diagnosis and treatment of skeletal muscle-related diseases. It is promising to enhance …
FPGA implementation of deep learning architecture for kidney cancer detection from histopathological images
Kidney cancer is the most common type of cancer, and designing an automated system to
accurately classify the cancer grade is of paramount importance for a better prognosis of the …
accurately classify the cancer grade is of paramount importance for a better prognosis of the …
Classification and grade prediction of kidney cancer histological images using deep learning
Abstract Renal Cell Carcinoma (RCC) is the most common malignant tumor (85%) of kidney
cancer and has a complex histological pattern and nuclear structure. The manual diagnosis …
cancer and has a complex histological pattern and nuclear structure. The manual diagnosis …