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Found 6 papers, 3 papers with code
NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning
To distinguish different levels of malignancy within specific regions of the slide, we include the origins of the tiles in our analysis.
Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images
We present a pioneering investigation into the application of deep learning techniques to analyze histopathological images for addressing the substantial challenge of automated prognostic prediction.
A Dual Convolutional Neural Network Pipeline for Melanoma Diagnostics and Prognostics
Melanoma is a type of cancer that begins in the cells controlling the pigment of the skin, and it is often referred to as the most dangerous skin cancer.
Deep Learning for Predicting Metastasis on Melanoma WSIs
Melanoma prognosis is based on a pathologist's subjective visual analysis of the patient's tumor.
Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images
Accurate segmentation of tissue in histopathological images can be very beneficial for defining regions of interest (ROI) for streamline of diagnostic and prognostic tasks.
Nested Multiple Instance Learning with Attention Mechanisms
Strongly supervised learning requires detailed knowledge of truth labels at instance levels, and in many machine learning applications this is a major drawback.
