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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning to diagnose from scratch by exploiting dependencies among labels · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1710.10501/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1710.10501/feed.xml" rel="self" type="application/rss+xml" /><item><title>Learning to diagnose from scratch by exploiting dependencies among labels — mentioned by bitL</title><link>https://news.ycombinator.com/item?id=17197213</link><guid isPermaLink="false">urn:hackerpapers:mention:1710.10501:17197213</guid><pubDate>Thu, 31 May 2018 15:50:50 GMT</pubDate><description>&lt;p&gt;There is a paper discussing higher-order interdependencies between diagnoses [1] on X-ray images (they seem to apply LSTM to derive those dependencies). This could be probably extended to include data outside X-ray images. My take is that it&amp;#x27;s pretty impressive what we can derive from a single image; now if we have multiple low-level Deep Learning-based diagnostic subsystems and combine them together via some glue (either Deep (Reinforcement) Learning, classical ML, logic-based expert system, PGM etc.), we might be able to represent/identify diagnoses with much more certainty than any single…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1710.10501/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
