AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis
While alterations in nucleus size, shape, and color are ubiquitous in cancer, comprehensive
quantification of nuclear morphology across a whole-slide histologic image remains a …
quantification of nuclear morphology across a whole-slide histologic image remains a …
Interpretability analysis on a pathology foundation model reveals biologically relevant embeddings across modalities
Mechanistic interpretability has been explored in detail for large language models (LLMs).
For the first time, we provide a preliminary investigation with similar interpretability methods …
For the first time, we provide a preliminary investigation with similar interpretability methods …
stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multi-modal feature representation
Deciphering spatial domains using spatially resolved transcriptomics (SRT) is of great value
for the characterizing and understanding of tissue architecture. However, the inherent …
for the characterizing and understanding of tissue architecture. However, the inherent …
Deep-learning quantified cell-type-specific nuclear morphology predicts genomic instability and prognosis in multiple cancer types
Background Altered nuclei are ubiquitous in cancer, with changes in nuclear size, shape,
and coloration accompanying cancer progression. However, comprehensive quantification …
and coloration accompanying cancer progression. However, comprehensive quantification …