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Found 4 papers, 2 papers with code
Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction
Necrosis is routinely assessed post-chemotherapy from histology slides on resection specimens where necrosis ratio is defined as the ratio of necrotic tumor to overall tumor.
Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation
Instead of annotating all pixels from cancer and non-cancer regions on giga-pixel whole slide images, an iterative process of annotating mislabeled regions from a segmentation model and training/finetuning the model with the additional annotation can reduce the time.
Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment
Osteosarcoma is the most common malignant primary bone tumor.
Beyond Classification: Whole Slide Tissue Histopathology Analysis By End-To-End Part Learning
A loss with respect to the slide label is backpropagated through an integrated CNN model to $k$ input tiles that are used to represent each part.
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