24 citations · 35 across the 4 of their papers we have counts for
5 papers
Morphology-aware deep learning predicts colorectal cancer survival from whole-slide images
Hikmat Khan, Metin N. Gurcan, Anil Parwani +3
Background: Five-year survival prediction from hematoxylin-and-eosin whole-slide images could support risk stratification, but the task is weakly supervised, and the tissue morphol…
Machine Learning-Based Analysis of Ebola Virus' Impact on Gene Expression in Nonhuman Primates
Mostafa Rezapour, Muhammad Khalid Khan Niazi, Hao Lu +2
This study introduces the Supervised Magnitude-Altitude Scoring (SMAS) methodology, a machine learning-based approach, for analyzing gene expression data obtained from nonhuman pri…
Cross-attention-based saliency inference for predicting cancer metastasis on whole slide images
Ziyu Su, Mostafa Rezapour, Usama Sajjad +3
Although multiple instance learning (MIL) methods are widely used for automatic tumor detection on whole slide images (WSI), they suffer from the extreme class imbalance within the…
Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images
Ziyu Su, Mostafa Rezapour, Usama Sajjad +2
Multiple instance learning (MIL) models have achieved remarkable success in analyzing whole slide images (WSIs) for disease classification problems. However, with regard to gigapix…
A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRI
Qing Lyu, Sanjeev V. Namjoshi, Emory McTyre +17
Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site, and currently made with biopsy and histology. Here we develop a novel deep learning ap…