Multi-task Learning for Melanoma Detection with Julianna Ianni - #531

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

内容由TWIML and Sam Charrington提供。所有播客内容（包括剧集、图形和播客描述）均由 TWIML and Sam Charrington 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品，您可以按照此处概述的流程进行操作https://zh.player.fm/legal。

2+ y ago 37:33

MP3•单集首页

In today’s episode, we are joined by Julianna Ianni, vice president of AI research & development at Proscia.

In our conversation, Julianna shares her and her team’s research focused on developing applications that would help make the life of pathologists easier by enabling tasks to quickly and accurately be diagnosed using deep learning and AI.

We also explore their paper “A Pathology Deep Learning System Capable of Triage of Melanoma Specimens Utilizing Dermatopathologist Consensus as Ground Truth”, while talking through how ML aids pathologists in diagnosing Melanoma by building a multitask classifier to distinguish between low-risk and high-risk cases. Finally, we discussed the challenges involved in designing a model that would help in identifying and classifying Melanoma, the results they’ve achieved, and what the future of this work could look like.

The complete show notes for this episode can be found at twimlai.com/go/531.

700集单集