… and therapeutic outcome of tumor characteristics in a noninvasive … –small cell lung cancer
to identify tumor histopathology. We … The obtained CT image is segmented to define the tumor …

… classification of histopathological images related to lung tissues. … the contrast of the
histopathological images, which are … image vectors from the segmented histopathology images. …

… a single CNN architecture for both segmentation and classification. Due to … cancer
histopathological images in this research contribution. Automatic segmentation of medical images …

… analysis software already incorporates machine learning algorithms to assist researchers
and clinicians in quantifying and segmenting histopathological images. These tools have …

… We propose a framework for the segmentation of microvessels in H&E stained histology
images. The framework extends the DeepLabV3+ network architecture by utilizing a modified …

… in the process of histopathological images segmentation, a two-stage nuclei segmentation
strategy, that is, a method of watershed segmentation based on histopathological images …

… In the future, we aim to combine the proposed method with a semantic segmentation technique
in order to allow the CNN extract features from areas that are more specific to the actual …

… as in cancer progression and metastasis in lung cancer. Several studies have aimed to develop
algorithms for TME characterization of lung cancer pathology images to … Segmentation of …

… method using the LC25000 histopathology imaging dataset of lung cancer, which was …
techniques and segmentation algorithms can be employed on the images to enhance the …

… In contrast, the method proposed here extracts the representative characteristics of patches
segmented from liver histopathological images via transfer learning and preserves the patch …