Topology aware fully convolutional networks for histology gland segmentation
A BenTaieb, G Hamarneh - … conference on medical image computing and …, 2016 - Springer
The recent success of deep learning techniques in classification and object detection tasks
has been leveraged for segmentation tasks. However, a weakness of these deep …
has been leveraged for segmentation tasks. However, a weakness of these deep …
Multi-scale context-guided deep network for automated lesion segmentation with endoscopy images of gastrointestinal tract
Accurate lesion segmentation based on endoscopy images is a fundamental task for the
automated diagnosis of gastrointestinal tract (GI Tract) diseases. Previous studies usually …
automated diagnosis of gastrointestinal tract (GI Tract) diseases. Previous studies usually …
Discriminative error prediction network for semi-supervised colon gland segmentation
Pixel-wise error correction of initial segmentation results provides an effective way for quality
improvement. The additional error segmentation network learns to identify correct …
improvement. The additional error segmentation network learns to identify correct …
Improving nuclei/gland instance segmentation in histopathology images by full resolution neural network and spatial constrained loss
Image segmentation plays an important role in pathology image analysis as the accurate
separation of nuclei or glands is crucial for cancer diagnosis and other clinical analyses. The …
separation of nuclei or glands is crucial for cancer diagnosis and other clinical analyses. The …
Multi-scale fully convolutional network for gland segmentation using three-class classification
H Ding, Z Pan, Q Cen, Y Li, S Chen - Neurocomputing, 2020 - Elsevier
Automated precise segmentation of glands from the histological images plays an important
role in glandular morphology analysis, which is a crucial criterion for cancer grading and …
role in glandular morphology analysis, which is a crucial criterion for cancer grading and …
Neural ordinary differential equations for semantic segmentation of individual colon glands
H Pinckaers, G Litjens - arXiv preprint arXiv:1910.10470, 2019 - arxiv.org
Automated medical image segmentation plays a key role in quantitative research and
diagnostics. Convolutional neural networks based on the U-Net architecture are the state-of …
diagnostics. Convolutional neural networks based on the U-Net architecture are the state-of …
Cgc-net: Cell graph convolutional network for grading of colorectal cancer histology images
Colorectal cancer (CRC) grading is typically carried out by assessing the degree of gland
formation within histology images. To do this, it is important to consider the overall tissue …
formation within histology images. To do this, it is important to consider the overall tissue …
DCAN: Deep contour-aware networks for object instance segmentation from histology images
In histopathological image analysis, the morphology of histological structures, such as
glands and nuclei, has been routinely adopted by pathologists to assess the malignancy …
glands and nuclei, has been routinely adopted by pathologists to assess the malignancy …
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 …
Current trends of artificial intelligence for colorectal cancer pathology image analysis: a systematic review
Colorectal cancer (CRC) is one of the most common cancers requiring early pathologic
diagnosis using colonoscopy biopsy samples. Recently, artificial intelligence (AI) has made …
diagnosis using colonoscopy biopsy samples. Recently, artificial intelligence (AI) has made …