4 citations · 5 across the 6 of their papers we have counts for
6 papers
NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning
Saul Fuster, Umay Kiraz, Trygve Eftestøl +2
The most prevalent form of bladder cancer is urothelial carcinoma, characterized by a high recurrence rate and substantial lifetime treatment costs for patients. Grading is a prime…
Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images
Saul Fuster, Farbod Khoraminia, Julio Silva-Rodríguez +7
We present a pioneering investigation into the application of deep learning techniques to analyze histopathological images for addressing the substantial challenge of automated pro…
A Dual Convolutional Neural Network Pipeline for Melanoma Diagnostics and Prognostics
Marie Bø-Sande, Edvin Benjaminsen, Neel Kanwal +5
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. Diagnosing melanoma can be…
Deep Learning for Predicting Metastasis on Melanoma WSIs
Christopher Andreassen, Saul Fuster, Helga Hardardottir +2
Northern Europe has the second highest mortality rate of melanoma globally. In 2020, the mortality rate of melanoma rose to 1.9 per 100 000 habitants. Melanoma prognosis is based o…
Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images
Saul Fuster, Farbod Khoraminia, Trygve Eftestøl +2
Accurate segmentation of tissue in histopathological images can be very beneficial for defining regions of interest (ROI) for streamline of diagnostic and prognostic tasks. Still,…
Nested Multiple Instance Learning with Attention Mechanisms
Saul Fuster, Trygve Eftestøl, Kjersti Engan
Strongly supervised learning requires detailed knowledge of truth labels at instance levels, and in many machine learning applications this is a major drawback. Multiple instance l…