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Found 7 papers, 3 papers with code
Stain-invariant self supervised learning for histopathology image analysis
Our method achieves the state-of-the-art performance on several publicly available breast cancer datasets ranging from tumor classification (CAMELYON17) and subtyping (BRACS) to HER2 status classification and treatment response prediction.
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.
Deep Multi-Magnification Networks for Multi-Class Breast Cancer Image Segmentation
Pathologic analysis of surgical excision specimens for breast carcinoma is important to evaluate the completeness of surgical excision and has implications for future treatment.
Center-Extraction-Based Three Dimensional Nuclei Instance Segmentation of Fluorescence Microscopy Images
Fluorescence microscopy is an essential tool for the analysis of 3D subcellular structures in tissue.
Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation
Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue.
