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Found 5 papers, 2 papers with code
Correlated Feature Aggregation by Region Helps Distinguish Aggressive from Indolent Clear Cell Renal Cell Carcinoma Subtypes on CT
CorrFABR consists of three main steps: (1) Feature Aggregation where region-level features are extracted from radiology and pathology images, (2) Fusion where radiology features correlated with pathology features are learned on a region level, and (3) Prediction where the learned correlated features are used to distinguish aggressive from indolent clear cell RCC using CT alone as input.
Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications
We bring forward a number of considerations, such as view generation for the contrastive objective and hyper-parameter tuning.
Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection
The scarcity of labeled data is a major bottleneck for developing accurate and robust deep learning-based models for histopathology applications.
A Study of Deep Learning Colon Cancer Detection in Limited Data Access Scenarios
One such scenario relates to detecting tumor metastasis in lymph node tissue, where the low ratio of tumor to non-tumor cells makes the diagnostic task hard and time-consuming.
A Closer Look at Domain Shift for Deep Learning in Histopathology
Most centrally, we present a novel measure for evaluating the distance between domains in the context of the learned representation of a particular model.
