Jul 2, 2020 · In this paper, we describe Deep Interactive Learning (DIaL) as an efficient labeling approach for training CNNs.

Sep 29, 2020 · In this paper, we describe Deep Interactive Learning (DIaL) as an efficient labeling approach for training CNNs.

The experiments show that the CNN model trained by only 7 hours of annotation using DIaL can successfully estimate ratios of necrosis within expected ...

Sep 8, 2024 · Convolutional neural networks (CNNs) can be used for automated segmentation of viable and necrotic tumor on osteosarcoma whole slide images. One ...

This study indicates that deep learning can support pathologists as an objective tool to analyze osteosarcoma from histology for assessing treatment response ...

Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment · no code implementations • 2 Jul ...

Deep interactive learning: an efficient labeling approach for deep learning-based osteosarcoma treatment response assessment. DJ Ho, NP Agaram, PJ Schüffler ...

Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment. Chapter. Sep 2020.

Deep learning can support pathologists as an objective tool to analyze osteosarcoma from histology for assessing treatment response and predicting patient ...

Deep interactive learning: An efficient labeling approach for deep learning-based osteosarcoma treatment response assessment. Proceedings of the Medical ...