Artificial intelligence for multimodal data integration in oncology
In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …
from radiology, histology, and genomics to electronic health records. Current artificial …
[HTML][HTML] Transformers in medical image analysis
Transformers have dominated the field of natural language processing and have recently
made an impact in the area of computer vision. In the field of medical image analysis …
made an impact in the area of computer vision. In the field of medical image analysis …
Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L) 1 blockade in patients with non-small cell lung cancer
Immunotherapy is used to treat almost all patients with advanced non-small cell lung cancer
(NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we …
(NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we …
Federated learning and differential privacy for medical image analysis
The artificial intelligence revolution has been spurred forward by the availability of large-
scale datasets. In contrast, the paucity of large-scale medical datasets hinders the …
scale datasets. In contrast, the paucity of large-scale medical datasets hinders the …
Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning
We address the challenging problem of whole slide image (WSI) classification. WSIs have
very high resolutions and usually lack localized annotations. WSI classification can be cast …
very high resolutions and usually lack localized annotations. WSI classification can be cast …
Hopfield networks is all you need
H Ramsauer, B Schäfl, J Lehner, P Seidl… - ar**s as
possible between the training data and outputs, where each training data will predict as a …
possible between the training data and outputs, where each training data will predict as a …
Pooling in convolutional neural networks for medical image analysis: a survey and an empirical study
Convolutional neural networks (CNN) are widely used in computer vision and medical
image analysis as the state-of-the-art technique. In CNN, pooling layers are included mainly …
image analysis as the state-of-the-art technique. In CNN, pooling layers are included mainly …