HistoMIL: A Python package for training multiple instance learning models on histopathology slides

Pan, Shi; Secrier, Maria

doi:10.1016/j.isci.2023.108073

Cited by 3 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Slideflow [66] is a deep learning library for histopathology, supporting a wide variety of deep learning approaches, that includes a whole slide interface for deploying trained models. HistoMIL [67] is a Python package for training multiple instance learning models on histopathology slides designed to streamline the implementation, training, and inference process of MIL-based algorithms for computational pathologists.…”

Section: Other Toolsmentioning

confidence: 99%

The Quest for the Application of Artificial Intelligence to Whole Slide Imaging: Unique Prospective from New Advanced Tools

Faa,

Castagnola,

Didaci

et al. 2024

Algorithms

View full text Add to dashboard Cite

The introduction of machine learning in digital pathology has deeply impacted the field, especially with the advent of whole slide image (WSI) analysis. In this review, we tried to elucidate the role of machine learning algorithms in diagnostic precision, efficiency, and the reproducibility of the results. First, we discuss some of the most used tools, including QuPath, HistoQC, and HistomicsTK, and provide an updated overview of machine learning approaches and their application in pathology. Later, we report how these tools may simplify the automation of WSI analyses, also reducing manual workload and inter-observer variability. A novel aspect of this review is its focus on open-source tools, presented in a way that may help the adoption process for pathologists. Furthermore, we highlight the major benefits of these technologies, with the aim of making this review a practical guide for clinicians seeking to implement machine learning-based solutions in their specific workflows. Moreover, this review also emphasizes some crucial limitations related to data quality and the interpretability of the models, giving insight into future directions for research. Overall, this work tries to bridge the gap between the more recent technological progress in computer science and traditional clinical practice, supporting a broader, yet smooth, adoption of machine learning approaches in digital pathology.

show abstract

Section: Other Toolsmentioning

confidence: 99%