A guide to artificial intelligence for cancer researchers
Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to
a readily accessible tool for cancer researchers. AI-based tools can boost research …
a readily accessible tool for cancer researchers. AI-based tools can boost research …
From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology
Hematoxylin-and eosin-stained whole-slide images (WSIs) are the foundation of diagnosis
of cancer. In recent years, development of deep learning-based methods in computational …
of cancer. In recent years, development of deep learning-based methods in computational …
A foundation model for clinical-grade computational pathology and rare cancers detection
The analysis of histopathology images with artificial intelligence aims to enable clinical
decision support systems and precision medicine. The success of such applications …
decision support systems and precision medicine. The success of such applications …
Towards a general-purpose foundation model for computational pathology
Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …
requiring the objective characterization of histopathological entities from whole-slide images …
A multimodal generative AI copilot for human pathology
The field of computational pathology [1, 2] has witnessed remarkable progress in the
development of both task-specific predictive models and task-agnostic self-supervised vision …
development of both task-specific predictive models and task-agnostic self-supervised vision …
A pathology foundation model for cancer diagnosis and prognosis prediction
Histopathology image evaluation is indispensable for cancer diagnoses and subtype
classification. Standard artificial intelligence methods for histopathology image analyses …
classification. Standard artificial intelligence methods for histopathology image analyses …
THItoGene: a deep learning method for predicting spatial transcriptomics from histological images
Y Jia, J Liu, L Chen, T Zhao… - Briefings in Bioinformatics, 2024 - academic.oup.com
Spatial transcriptomics unveils the complex dynamics of cell regulation and transcriptomes,
but it is typically cost-prohibitive. Predicting spatial gene expression from histological images …
but it is typically cost-prohibitive. Predicting spatial gene expression from histological images …
Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning
F Tian, D Liu, N Wei, Q Fu, L Sun, W Liu, X Sui… - Nature Medicine, 2024 - nature.com
Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive
nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging …
nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging …
A general-purpose self-supervised model for computational pathology
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning
objective characterizations of histopathologic biomarkers in anatomic pathology. However …
objective characterizations of histopathologic biomarkers in anatomic pathology. However …
Prediction of recurrence risk in endometrial cancer with multimodal deep learning
S Volinsky-Fremond, N Horeweg, S Andani… - Nature Medicine, 2024 - nature.com
Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant
treatment. The current gold standard of combined pathological and molecular profiling is …
treatment. The current gold standard of combined pathological and molecular profiling is …