CancerSiamese: one-shot learning for predicting primary and metastatic tumor types unseen during model training

Mostavi, Milad; Chiu, Yu‐Chiao; Chen, Yidong; Huang, Yufei

doi:10.1101/2020.09.07.286583

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…However, given the scarcity of data on less common lesions (serrated adenomas) and knowing that deep learning approaches require vast numbers of labelled training samples, new research may include techniques such as few-shot learning introduced by Vinyals et al [ 74 ]. This technique focuses on learning a class from one or a few labelled samples and has been successfully applied in other medical areas, such as cervical cancer cell classification[ 75 ], breast cancer classification[ 76 ], and metastatic tumour classification[ 77 ].…”

Section: Future Prospectsmentioning

confidence: 99%

Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions

Viscaíno¹,

Bustos²,

Muñóz³

et al. 2021

WJG

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Colorectal cancer (CRC) was the second-ranked worldwide type of cancer during 2020 due to the crude mortality rate of 12.0 per 100000 inhabitants. It can be prevented if glandular tissue (adenomatous polyps) is detected early. Colonoscopy has been strongly recommended as a screening test for both early cancer and adenomatous polyps. However, it has some limitations that include the high polyp miss rate for smaller (< 10 mm) or flat polyps, which are easily missed during visual inspection. Due to the rapid advancement of technology, artificial intelligence (AI) has been a thriving area in different fields, including medicine. Particularly, in gastroenterology AI software has been included in computer-aided systems for diagnosis and to improve the assertiveness of automatic polyp detection and its classification as a preventive method for CRC. This article provides an overview of recent research focusing on AI tools and their applications in the early detection of CRC and adenomatous polyps, as well as an insightful analysis of the main advantages and misconceptions in the field.

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Section: Future Prospectsmentioning

confidence: 99%

Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions

Viscaíno¹,

Bustos²,

Muñóz³

et al. 2021

WJG

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“…Tabular data is probably the most used data type within clinical research. However, we only identified 15 studies using transfer learning on tabular data covering very different fields in medicine: two-thirds of them were from genetics [98][99][100][101][102], pathology [103][104][105], and intensive care [18,106], while the remaining five were from surgery [17], neonatology [107], infectious disease [108], pulmonology [109], and pharmacology [110]. Oncological applications like classification of cancer and prediction of cancer survival were common among the studies in genetics or pathology.…”

Section: Tabular Datamentioning

confidence: 99%

Transfer learning for non-image data in clinical research: a scoping review

Ebbehøj

Thunbo

Andersen

et al. 2021

Preprint

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Background Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature. Methods and Findings We systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical studies that used transfer learning on human non-image data. We included 83 studies in the review. More than half of the studies (63%) were published within 12 months of the search. Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). Thirty-three (40%) studies applied an image-based model to non-image data after transforming data into images (e.g. spectrograms). Twenty-nine (35%) studies did not have any authors with a health-related affiliation. Many studies used publicly available datasets (66%) and models (49%), but fewer shared their code (27%). Conclusions In this scoping review, we have described current trends in the use of transfer learning for non-image data in the clinical literature. We found that the use of transfer learning has grown rapidly within the last few years. We have identified studies and demonstrated the potential of transfer learning in clinical research in a wide range of medical specialties. More interdisciplinary collaborations and the wider adaption of reproducible research principles are needed to increase the impact of transfer learning in clinical research.

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“…Deep learning (DL) is a class of machine learning (ML) methods that uses multilayered neural networks to extract high-order features. DL is increasingly being used in genomics research for cancer survival (11,12) and cancer classification (13)(14)(15). DL methods have also been applied to pharmacogenomics for predicting drug sensitivity and synergy (16,17).…”

Section: Introductionmentioning

confidence: 99%

Predicting and characterizing a cancer dependency map of tumors with deep learning

Chiu¹,

Zheng²,

Wang³

et al. 2021

Sci. Adv.

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Genome-wide loss-of-function screens have revealed genes essential for cancer cell proliferation, called cancer dependencies. It remains challenging to link cancer dependencies to the molecular compositions of cancer cells or to unscreened cell lines and further to tumors. Here, we present DeepDEP, a deep learning model that predicts cancer dependencies using integrative genomic profiles. It uses a unique unsupervised pretraining that captures unlabeled tumor genomic representations to improve the learning of cancer dependencies. We demonstrated DeepDEP's improvement over conventional machine learning methods and validated the performance with three independent datasets. By systematic model interpretations, we extended the current dependency maps with functional characterizations of dependencies and a proof-of-concept in silico assay of synthetic essentiality. We applied DeepDEP to pan-cancer tumor genomics and built the first pan-cancer synthetic dependency map of 8000 tumors with clinical relevance. In summary, DeepDEP is a novel tool for investigating cancer dependency with rapidly growing genomic resources.

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CancerSiamese: one-shot learning for predicting primary and metastatic tumor types unseen during model training

Cited by 5 publications

References 35 publications

Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions

Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions

Transfer learning for non-image data in clinical research: a scoping review

Predicting and characterizing a cancer dependency map of tumors with deep learning

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