2018英特尔ISEF获奖作品摘要 机器人与智能机器（人工智能） Intel Finalist Abstract Robotics and Intelligent Machines (AI)

获奖作品基本信息

年份	2018
学科	机器人与智能机器 Robotics and Intelligent Machines
国家/州	United States of America

获奖作品名称

Deep, Multimodal Representation Learning for Pan-Cancer Prognosis Prediction Robotics and Intelligent Machines

获奖作品摘要

Estimating the future course of cancer is invaluable to physicians; however, current clinical methods fail to effectively use the vast amount of multimodal data available.

To tackle this problem, I constructed a deep neural network model to predict the survival of patients for 33 different cancer types, using gene expressions, miRNA data, clinical data and histopathology images. I developed an unsupervised encoder to compress these four data modalities into a single feature vector for each patient, handling missing data through a resilient, multimodal dropout method. Encoding methods were tailored to each data type - using Dilated DCNNS (Deep Convolutional Neural Networks) to summarize gigapixel-resolution pathology images and using vanilla feedforward networks to extract deep features from genetic and clinical data. I then used these feature encodings to predict survival data, achieving an impressive 0.754 C-index.

This research was the first attempt to build a pan-cancer prognosis model - all previous research focused on cancer-specific datasets. Furthermore, my model handles multiple data modalities, efficiently analyzes huge whole-slide images, and summarizes patient details flexibly into an unsupervised, informative profile. I present a powerful automated tool to accurately determine prognosis, a key step towards personalized treatment for cancer patients.

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高中生科研 英特尔 Intel ISEF
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高中生科研学术活动 英特尔 Intel ISEF 简介

英特尔国际科学与工程大奖赛，简称 "ISEF"，由美国 Society for Science and the Public（科学和公共服务协会）主办，英特尔公司冠名赞助，是全球规模最大、等级最高的中学生的科研科创赛事。ISEF 的学术活动学科包括了所有数学、自然科学、工程的全部领域和部分社会科学。ISEF 素有全球青少年科学学术活动的“世界杯”之美誉，旨在鼓励学生团队协作，开拓创新，长期专一深入地研究自己感兴趣的课题。

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英特尔 ISEF 学术活动详细介绍

英特尔 ISEF 全程指导方案

· 数学 · 物理 · 化学 · 生物 · 计算机 · 工程 ·

学科简介：机器人与智能机器 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.