Advancements in Digital Pathology and Image-Based Profiling

The field of digital pathology and image-based profiling is witnessing significant advancements with the integration of machine learning and deep learning techniques. Researchers are exploring innovative methods to improve the efficiency and accuracy of image classification, segmentation, and feature extraction. One of the key directions is the development of uncertainty-aware models that can selectively label the most crucial images, reducing the need for extensive labeled datasets. Another area of focus is the application of non-contrastive self-supervised learning methods for image-based profiling, which has shown promising results in cell image analysis. The use of domain adaptation techniques is also gaining traction, enabling the effective transfer of knowledge across different domains and improving the robustness of models. Noteworthy papers in this area include:

  • Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology, which introduces a novel approach to selectively label images using uncertainty scores.
  • OpenPath, a novel open-set active learning approach for pathological image classification that leverages pre-trained vision-language models to select informative samples.
  • Exploring Non-contrastive Self-supervised Representation Learning for Image-based Profiling, which proposes a specialized framework for cell image analysis using non-contrastive self-supervised learning methods.

Sources

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology

Predicting Patient Survival with Airway Biomarkers using nn-Unet/Radiomics

One-Shot Neural Architecture Search with Network Similarity Directed Initialization for Pathological Image Classification

Exploring Non-contrastive Self-supervised Representation Learning for Image-based Profiling

Detecting immune cells with label-free two-photon autofluorescence and deep learning

DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classification

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing

OpenPath: Open-Set Active Learning for Pathology Image Classification via Pre-trained Vision-Language Models

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