Artificial intelligence in histopathology: enhancing cancer research and clinical oncology

Nature Cancer, Journal Year: 2022, Volume and Issue: 3(9), P. 1026 - 1038

Published: Sept. 22, 2022

Language: Английский

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Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer

Nature Medicine, Journal Year: 2023, Volume and Issue: 29(2), P. 430 - 439

Published: Jan. 9, 2023

Language: Английский

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Denoising diffusion probabilistic models for 3D medical image generation

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: May 5, 2023

Abstract Recent advances in computer vision have shown promising results image generation. Diffusion probabilistic models generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen, and Stable Diffusion. However, their use medicine, where imaging data typically comprises three-dimensional volumes, has not been systematically evaluated. Synthetic may play a crucial role privacy-preserving artificial intelligence can also be used to augment small datasets. We show that diffusion synthesize high-quality medical for magnetic resonance (MRI) computed tomography (CT). For quantitative evaluation, two radiologists rated the quality of synthesized regarding "realistic appearance", "anatomical correctness", "consistency between slices". Furthermore, we demonstrate synthetic self-supervised pre-training improve performance breast segmentation when is scarce (Dice scores, 0.91 [without data], 0.95 [with data]).

Language: Английский

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Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer

Nature Medicine, Journal Year: 2023, Volume and Issue: 29(1), P. 135 - 146

Published: Jan. 1, 2023

Language: Английский

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Artificial intelligence for digital and computational pathology

Nature Reviews Bioengineering, Journal Year: 2023, Volume and Issue: 1(12), P. 930 - 949

Published: Oct. 2, 2023

Language: Английский

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Privacy and Security in Federated Learning: A Survey

Applied Sciences, Journal Year: 2022, Volume and Issue: 12(19), P. 9901 - 9901

Published: Oct. 1, 2022

In recent years, privacy concerns have become a serious issue for companies wishing to protect economic models and comply with end-user expectations. the same vein, some countries now impose, by law, constraints on data use protection. Such context thus encourages machine learning evolve from centralized computation approach decentralized approaches. Specifically, Federated Learning (FL) has been recently developed as solution improve privacy, relying local train models, which collaborate update global model that improves generalization behaviors. However, definition, no computer system is entirely safe. Security issues, such poisoning adversarial attack, can introduce bias in predictions. addition, it shown reconstruction of private raw still possible. This paper presents comprehensive study concerning various security issues related federated learning. Then, we identify state-of-the-art approaches aim counteract these problems. Findings our confirm current major threats are poisoning, backdoor, Generative Adversarial Network (GAN)-based attacks, while inference-based attacks most critical FL. Finally, ongoing research directions topic. could be used reference promote cybersecurity-related designing FL-based solutions alleviating future challenges.

Language: Английский

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Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology

npj Precision Oncology, Journal Year: 2023, Volume and Issue: 7(1)

Published: March 28, 2023

The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep learning can predict alterations from pathology slides, but it is unclear how well these predictions generalize to external datasets. We performed a systematic study on Deep-Learning-based prediction histology, using two large datasets multiple tumor types. show that an analysis pipeline integrates self-supervised feature extraction and attention-based instance achieves robust predictability generalizability.

Language: Английский

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Toward Explainable Artificial Intelligence for Precision Pathology

Annual Review of Pathology Mechanisms of Disease, Journal Year: 2023, Volume and Issue: 19(1), P. 541 - 570

Published: Oct. 23, 2023

The rapid development of precision medicine in recent years has started to challenge diagnostic pathology with respect its ability analyze histological images and increasingly large molecular profiling data a quantitative, integrative, standardized way. Artificial intelligence (AI) and, more precisely, deep learning technologies have recently demonstrated the potential facilitate complex analysis tasks, including clinical, histological, for disease classification; tissue biomarker quantification; clinical outcome prediction. This review provides general introduction AI describes developments focus on applications beyond. We explain limitations black-box character conventional describe solutions make machine decisions transparent so-called explainable AI. purpose is foster mutual understanding both biomedical side. To that end, addition providing an overview relevant foundations learning, we present worked-through examples better practical what can achieve how it should be done.

Language: Английский

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Computational Pathology: A Survey Review and The Way Forward

Journal of Pathology Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 100357 - 100357

Published: Jan. 1, 2024

Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath develop infrastructure workflows digital diagnostics as assistive CAD system clinical pathology, facilitating transformational changes in the diagnosis treatment cancer are mainly address by tools. With evergrowing deep learning computer vision algorithms, ease data flow from currently witnessing a paradigm shift. Despite sheer volume engineering scientific works being introduced image analysis, there still considerable gap adopting integrating these algorithms practice. This raises significant question regarding direction trends undertaken CPath. In this article we provide comprehensive review more than 800 papers challenges faced problem design all-the-way application implementation viewpoints. We have catalogued each paper into model-card examining key layout current landscape hope helps community locate relevant facilitate understanding field's future directions. nutshell, oversee cycle stages which required be cohesively linked together associated with such multidisciplinary science. overview different perspectives data-centric, model-centric, application-centric problems. finally sketch remaining directions technical integration For updated information on survey accessing original cards repository, please refer GitHub. Updated version draft can also found arXiv.

Language: Английский

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Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions

Cancer Discovery, Journal Year: 2024, Volume and Issue: 14(5), P. 711 - 726

Published: March 21, 2024

Artificial intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of field, with a specific focus on integration. AI applications are structured according cancer type and domain, focusing four most common cancers tasks detection, diagnosis, treatment. These encompass various data modalities, including imaging, genomics, medical records. We conclude summary existing challenges, evolving solutions, potential future directions for field.

Language: Английский

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