2017英特爾ISEF獲獎作品摘要 機器人與智能機器（人工智能） Intel Finalist Abstract Robotics and Intelligent Machines (AI)

獲獎作品基本信息

年份	2017
學科	機器人與智能機器 Robotics and Intelligent Machines
國家/州	United States of America

獲獎作品名稱

Mitosis Detection and Tumor Grading Using Deep Convolutional Neural Networks

獲獎作品摘要

Analyzing mitosis proliferation is crucial to physician grading of tumor severity and prognosis. Despite the time and effort poured into automated mitosis detection, no systems have been able to perform at the level of skilled pathologists, who manually examine slide images for mitotic cells.

Deep learning is an emerging field of machine learning based on modeling high-level abstractions using large interconnected networks of neurons. We build a custom deep convolutional network architecture, based on the seminal GoogleNet model and principles of aggressive, multiscale data augmentation. Since mitoses are extremely rare compared to normal cells, we also develop a novel multi-stage “bootstrapping” algorithm to extract meaningful training examples from highly imbalanced data. Our deep convolutional neural network achieves an F1 score of 0.85 on the 717-image TUPAC dataset. This trained model also generalizes well, surpassing the best F1-scores obtained in previous mitosis competitions.

We then develop a pipeline to grade whole slide images using an intuitive ROI selection algorithm and the trained mitosis detector. Our algorithms detect crucial regions within enormous whole slide images and use these regions to extrapolate levels of tumor-linked mitotic activity. The genes identified by our continuous mitosis scores outperform pathologist scores in their association to cancer and cell cycle functions/ pathways. Finally, we build a gene-level model to predict the mitosis scores from RNA- sequence data. From this model, we extract 10 gene predictions possibly linked to tumor mitotic activity.

We hope our research is a step towards a future where physicians use precise, deep- learning- enabled analyses of mitotic activity to diagnose and treat patients.

高中生科研學術活動 英特爾 Intel ISEF 簡介

英特爾國際科學與工程大獎賽，簡稱 "ISEF"，由美國 Society for Science and the Public（科學和公共服務協會）主辦，英特爾公司冠名贊助，是全球規模最大、等級最高的中學生的科研科創賽事。ISEF 的學術活動學科包括了所有數學、自然科學、工程的全部領域和部分社會科學。ISEF 素有全球青少年科學學術活動的“世界杯”之美譽，旨在鼓勵學生團隊協作，開拓創新，長期專一深入地研究自己感興趣的課題。

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英特爾 ISEF 學術活動詳細介紹

英特爾 ISEF 全程指導方案

· 數學 · 物理 · 化學 · 生物 · 計算機 · 工程 ·

學科簡介：機器人與智能機器 Robotics and Intelligent Machines

Studies in which the use of machine intelligence is paramount to reducing the reliance on human intervention.

Subcategories:

Biomechanics?(BIE):?Studies and apparatus which mimic the role of mechanics in biological systems.

Cognitive Systems?(COG):?Studies/apparatus that operate similarly to the ways humans think and process information. Systems that provide for increased interaction of people and machines to more naturally extend and magnify human expertise, activity, and cognition.

Control Theory?(CON):?Studies that explore the behavior of dynamical systems with inputs, and how their behavior is modified by feedback. ?This includes new theoretical results and the applications of new and established control methods, system modelling, identification and simulation, the analysis and design of control systems (including computer-aided design), and practical implementation.

Machine Learning?(MAC):?Construction and/or study of algorithms that can learn from data.

Robot Kinematics?(KIN):?The study of movement in robotic systems.

Other?(OTH):?Studies that cannot be assigned to one of the above subcategories.?If the project involves multiple subcategories, the principal subcategory should be chosen instead of Other.