Tumor-infiltrating lymphocytes in the immunotherapy era
ST Paijens, A Vledder, M de Bruyn… - Cellular & molecular …, 2021 - nature.com
The clinical success of cancer immune checkpoint blockade (ICB) has refocused attention
on tumor-infiltrating lymphocytes (TILs) across cancer types. The outcome of immune …
on tumor-infiltrating lymphocytes (TILs) across cancer types. The outcome of immune …
Deep learning in cancer pathology: a new generation of clinical biomarkers
Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers.
However, the growing number of these complex biomarkers tends to increase the cost and …
However, the growing number of these complex biomarkers tends to increase the cost and …
Multimodal co-attention transformer for survival prediction in gigapixel whole slide images
Survival outcome prediction is a challenging weakly-supervised and ordinal regression task
in computational pathology that involves modeling complex interactions within the tumor …
in computational pathology that involves modeling complex interactions within the tumor …
Vision Transformers in medical computer vision—A contemplative retrospection
Abstract Vision Transformers (ViTs), with the magnificent potential to unravel the information
contained within images, have evolved as one of the most contemporary and dominant …
contained within images, have evolved as one of the most contemporary and dominant …
Deep neural network models for computational histopathology: A survey
CL Srinidhi, O Ciga, AL Martel - Medical image analysis, 2021 - Elsevier
Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …
underlying mechanisms contributing to disease progression and patient survival outcomes …
Pan-cancer integrative histology-genomic analysis via multimodal deep learning
The rapidly emerging field of computational pathology has demonstrated promise in
developing objective prognostic models from histology images. However, most prognostic …
developing objective prognostic models from histology images. However, most prognostic …
Artificial intelligence for digital and computational pathology
Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …
including deep learning, have boosted the field of computational pathology. This field holds …
Whole slide images are 2d point clouds: Context-aware survival prediction using patch-based graph convolutional networks
Cancer prognostication is a challenging task in computational pathology that requires
context-aware representations of histology features to adequately infer patient survival …
context-aware representations of histology features to adequately infer patient survival …
Deep learning supported discovery of biomarkers for clinical prognosis of liver cancer
Tissue biomarkers are crucial for cancer diagnosis, prognosis assessment and treatment
planning. However, there are few known biomarkers that are robust enough to show true …
planning. However, there are few known biomarkers that are robust enough to show true …
A novel lightweight deep convolutional neural network for early detection of oral cancer
F Jubair, O Al‐karadsheh, D Malamos… - Oral …, 2022 - Wiley Online Library
Objectives To develop a lightweight deep convolutional neural network (CNN) for binary
classification of oral lesions into benign and malignant or potentially malignant using …
classification of oral lesions into benign and malignant or potentially malignant using …