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Found 9 papers, 5 papers with code
Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs
We developed DL pipelines using two MoEs and two multiclass models of state-of-the-art deep convolutional neural networks (DCNNs) and vision transformers (ViTs).
Ranked #1 on
Artifact Detection
on HistoArtifacts
(1:1 Accuracy metric)
Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images
We achieved 0. 996 and 0. 938 F1 scores for blur and folded tissue detection on unseen data, respectively.
Ranked #1 on
Artifact Detection
on HistoArtifacts
A Dual Convolutional Neural Network Pipeline for Melanoma Diagnostics and Prognostics
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.
Enhancing Prostate Cancer Diagnosis with Deep Learning: A Study using mpMRI Segmentation and Classification
Each segmentation model is paired with a different classifier to evaluate the performance using different metrics.
Balancing Privacy and Progress in Artificial Intelligence: Anonymization in Histopathology for Biomedical Research and Education
The advancement of biomedical research heavily relies on access to large amounts of medical data.
Vision Transformers for Small Histological Datasets Learned through Knowledge Distillation
Computational Pathology (CPATH) systems have the potential to automate diagnostic tasks.
Ranked #3 on
Artifact Detection
on HistoArtifacts
Detection and Localization of Melanoma Skin Cancer in Histopathological Whole Slide Images
Our method detects lesions with high accuracy and localizes them on a WSI to identify potential regions of interest for pathologists.
DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities
DID enables a more flexible and credible decentralized access management in our system, while the smart contract offers a frictionless and less error-prone process.
Attention-based Clinical Note Summarization
In recent years, the trend of deploying digital systems in numerous industries has hiked.
