The recent developments in the field of computational pathology and image segmentation have been marked by innovative approaches to tackle the challenges of noisy labels, class imbalance, and the need for high-quality segmentation in medical images. A significant trend is the shift towards weakly supervised and noise-tolerant methods that reduce the dependency on meticulously annotated datasets, which are often impractical to obtain, especially in medical imaging. Techniques leveraging superpixel clustering, robust sample selection, and adaptive noise-tolerant networks are at the forefront, aiming to refine segmentation boundaries and improve the accuracy of tumor microenvironment delineation. Moreover, the integration of quantum annealing and black-box optimization for filtering mislabeled instances introduces a novel dimension to enhancing dataset quality, showcasing the potential of quantum computing in machine learning tasks. These advancements not only address the limitations of existing methods but also open new avenues for research in unsupervised learning and large-scale implementations.

Noteworthy papers include:

• A multi-level superpixel correction algorithm that significantly improves tumor microenvironment boundary delineation in histopathology images.
• A method combining Robust Sample Selection and Margin-Guided Module to efficiently distinguish and process open set label noise, outperforming state-of-the-art label noise learning methods.
• The Collaborative Learning with Curriculum Selection framework, which addresses pixel-dependent noisy labels and class imbalance through a novel curriculum dynamic thresholding approach.
• An approach integrating surrogate model-based black-box optimization with quantum annealing for efficiently removing mislabeled instances from training datasets.
• The Adaptive Noise-Tolerant Network model, which integrates multiple noisy labels into a single deep learning model for superior image segmentation results.