Purpose Colorectal tumor segmentation is an important step in the analysis and diagnosis of
colorectal cancer. This task is a time consuming one since it is often performed manually by …

Histopathological image analysis is the gold standard for pathologists to grade colorectal
cancers of different differentiation types. However, the diagnosis by pathologists is highly …

Colonoscopy is considered the gold standard for colorectal cancer diagnosis and prognosis.
However, existing methods are less accurate and prone to overlooking lesions during …

With the long-term rapid increase in incidences of colorectal cancer (CRC), there is an
urgent clinical need to improve risk stratification. The conventional pathology report is …

Histopathology is the most accurate way to diagnose cancer and identify prognostic and
therapeutic targets. The likelihood of survival is significantly increased by early cancer …

Accurate classification of medical images is of great importance for correct disease
diagnosis. The automation of medical image classification is of great necessity because it …

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers and a leading
cause of cancer deaths in the United States. Colorectal polyps that grow on the intima of the …

The development of deep segmentation models for computational pathology (CPath) can
help foster the investigation of interpretable morphological biomarkers. Yet, there is a major …

Accurate segmentation of multiple organs of the head, neck, chest, and abdomen from
medical images is an essential step in computer-aided diagnosis, surgical navigation, and …

We present Scaden, a deep neural network for cell deconvolution that uses gene expression
information to infer the cellular composition of tissues. Scaden is trained on single-cell RNA …