One label is all you need: Interpretable AI-enhanced histopathology for oncology
Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to
benefit oncology through interpretable methods that require only one overall label per …
benefit oncology through interpretable methods that require only one overall label per …
Cross-Scale Fusion Transformer for Histopathological Image Classification
SK Huang, YT Yu, CR Huang… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Histopathological images provide the medical evidences to help the disease diagnosis.
However, pathologists are not always available or are overloaded by work. Moreover, the …
However, pathologists are not always available or are overloaded by work. Moreover, the …
Minimizing the intra-pathologist disagreement for tumor bud detection on H&E images using weakly supervised learning
TE Tavolara, W Chen, WL Frankel… - … 2023: Digital and …, 2023 - spiedigitallibrary.org
Tumor budding (TB) is defined as a cluster of one to four tumor cells at the tumor invasive
front. Though promising as a prognostic factor for colorectal cancer, its routine clinical use is …
front. Though promising as a prognostic factor for colorectal cancer, its routine clinical use is …
[HTML][HTML] Few-shot tumor bud segmentation using generative model in colorectal carcinoma
Current deep learning methods in histopathology are limited by the small amount of
available data and time consumption in labeling the data. Colorectal cancer (CRC) tumor …
available data and time consumption in labeling the data. Colorectal cancer (CRC) tumor …
Adapting SAM to histopathology images for tumor bud segmentation in colorectal cancer
Colorectal cancer (CRC) is the third most common cancer in the United States. Tumor
Budding (TB) detection and quantification are crucial yet labor-intensive steps in …
Budding (TB) detection and quantification are crucial yet labor-intensive steps in …