Semi-supervised pixel contrastive learning framework for tissue segmentation in histopathological image
Accurate tissue segmentation in histopathological images is essential for promoting the
development of precision pathology. However, the size of the digital pathological image is …
development of precision pathology. However, the size of the digital pathological image is …
Intra-and inter-pair consistency for semi-supervised gland segmentation
Accurate gland segmentation in histology tissue images is a critical but challenging task.
Although deep models have demonstrated superior performance in medical image …
Although deep models have demonstrated superior performance in medical image …
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 …
[HTML][HTML] Minimum resolution requirements of digital pathology images for accurate classification
L Neary-Zajiczek, L Beresna, B Razavi, V Pawar… - Medical Image …, 2023 - Elsevier
Digitization of pathology has been proposed as an essential mitigation strategy for the
severe staffing crisis facing most pathology departments. Despite its benefits, several …
severe staffing crisis facing most pathology departments. Despite its benefits, several …
Robust interactive semantic segmentation of pathology images with minimal user input
M Jahanifar, NZ Tajeddin… - Proceedings of the …, 2021 - openaccess.thecvf.com
From the simple measurement of tissue attributes in pathology workflow to designing an
explainable diagnostic/prognostic AI tool, access to accurate semantic segmentation of …
explainable diagnostic/prognostic AI tool, access to accurate semantic segmentation of …
Weakly Supervised Learning using Attention gates for colon cancer histopathological image segmentation
Abstract Recently, Artificial Intelligence namely Deep Learning methods have revolutionized
a wide range of domains and applications. Besides, Digital Pathology has so far played a …
a wide range of domains and applications. Besides, Digital Pathology has so far played a …
Gcsba-net: Gabor-based and cascade squeeze bi-attention network for gland segmentation
Colorectal cancer is the second and the third most common cancer in women and men,
respectively. Pathological diagnosis is the “gold standard” for tumor diagnosis. Accurate …
respectively. Pathological diagnosis is the “gold standard” for tumor diagnosis. Accurate …
Enabling a single deep learning model for accurate gland instance segmentation: A shape-aware adversarial learning framework
Segmenting gland instances in histology images is highly challenging as it requires not only
detecting glands from a complex background but also separating each individual gland …
detecting glands from a complex background but also separating each individual gland …
MTU: A multi-tasking U-net with hybrid convolutional learning and attention modules for cancer classification and gland Segmentation in Colon Histopathological …
A clinically comparable multi-tasking computerized deep U-Net-based model is
demonstrated in this paper. It intends to offer clinical gland morphometric information and …
demonstrated in this paper. It intends to offer clinical gland morphometric information and …
Pairwise relation learning for semi-supervised gland segmentation
Accurate and automated gland segmentation on histology tissue images is an essential but
challenging task in the computer-aided diagnosis of adenocarcinoma. Despite their …
challenging task in the computer-aided diagnosis of adenocarcinoma. Despite their …