3.8 Proceedings Paper

Visual Deep Learning-Based Explanation for Neuritic Plaques Segmentation in Alzheimer's Disease Using Weakly Annotated Whole Slide Histopathological Images

Related references

Note: Only part of the references are listed.
Review Physiology

From What to Why, the Growing Need for a Focus Shift Toward Explainability of AI in Digital Pathology

Samuel P. Border et al.

Summary: This article points out that although there have been significant performance gains in pathology due to deep learning and artificial intelligence techniques, little research has been done to answer the crucial question of why these algorithms make predictions. Tracing classification decisions back to specific input features allows for the identification of model bias and provides additional information for understanding underlying biological mechanisms. In digital pathology, increasing the explainability of AI models would have the largest and most immediate impact on image classification tasks. The article details some considerations that should be made in order to develop models with a focus on explainability.

FRONTIERS IN PHYSIOLOGY (2022)

Proceedings Paper Computer Science, Information Systems

Tau Protein Discrete Aggregates in Alzheimer's Disease: Neuritic Plaques and Tangles Detection and Segmentation using Computational Histopathology

K. Manouskova et al.

Summary: Tau proteins play a role in Alzheimer's disease, and detecting and segmenting the aggregates is crucial. This study presents a 5-step pipeline that improves state-of-the-art performances in detecting and segmenting tau protein aggregates, providing valuable insights in the field.

MEDICAL IMAGING 2022: DIGITAL AND COMPUTATIONAL PATHOLOGY (2022)

Article Neurosciences

Deep learning reveals disease-specific signatures of white matter pathology in tauopathies

Anthony R. Vega et al.

Summary: Although tauopathies are characterized by abnormal tau protein aggregation in both gray and white matter regions of the brain, neuropathological investigations have often focused on abnormalities in the cerebral cortex while white matter pathologies remain less well characterized. Machine learning techniques were used to identify disease-specific morphological signatures of white matter aggregates in AD, PSP, and CBD, revealing previously unrecognized tau morphologies and highlighting the informative nature of white matter tau pathology in disease classification.

ACTA NEUROPATHOLOGICA COMMUNICATIONS (2021)

Article Medicine, Research & Experimental

Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy

Maxim Signaevsky et al.

LABORATORY INVESTIGATION (2019)

Article Multidisciplinary Sciences

Automated acquisition of explainable knowledge from unannotated histopathology images

Yoichiro Yamamoto et al.

NATURE COMMUNICATIONS (2019)

Editorial Material Computer Science, Artificial Intelligence

Image analysis and machine learning in digital pathology: Challenges and opportunities

Anant Madabhushi et al.

MEDICAL IMAGE ANALYSIS (2016)

Review Clinical Neurology

Classification and basic pathology of Alzheimer disease

Charles Duyckaerts et al.

ACTA NEUROPATHOLOGICA (2009)