Motivation:　MEDLINE　is　the　primary　bibliographic　database　maintained　by　National　Library　of　Medicine　(NLM).　MEDLINE　citations　are　indexed　with　Medical　Subject　Headings　(MeSH),　which　is　a　controlled　vocabulary　curated　by　the　NLM　experts.　This　greatly　facilitates　the　applications　of　biomedical　research　and　knowledge　discovery.　Currently,　MeSH　indexing　is　manually　performed　by　human　experts.　To　reduce　the　time　and　monetary　cost　associated　with　manual　annotation,　many　automatic　MeSH　indexing　systems　have　been　proposed　to　assist　manual　annotation,　including　DeepMeSH　and　NLM＇s　official　model　Medical　Text　Indexer　(MTI).　However,　the　existing　models　usually　rely　on　the　intermediate　results　of　other　models　and　suffer　from　efficiency　issues.　We　propose　an　end-to-end　framework,　MeSHProbeNet　(formerly　named　as　xgx),　which　utilizes　deep　learning　and　self-attentive　MeSH　probes　to　index　MeSH　terms.　Each　MeSH　probe　enables　the　model　to　extract　one　specific　aspect　of　biomedical　knowledge　from　an　input　article,　thus　comprehensive　biomedical　information　can　be　extracted　with　different　MeSH　probes　and　interpretability　can　be　achieved　at　word　level.　MeSH　terms　are　finally　recommended　with　a　unified　classifier,　making　MeSHProbeNet　both　time　efficient　and　space　efficient.　Results:　MeSHProbeNet　won　the　first　place　in　the　latest　batch　of　Task　A　in　the　2018　BioASQ　challenge.　The　result　on　the　last　test　set　of　the　challenge　is　reported　in　this　paper.　Compared　with　other　state-of-the-art　models,　such　as　MTI　and　DeepMeSH,　MeSHProbeNet　achieves　the　highest　scores　in　all　the　F-measures,　including　Example　Based　F-Measure,　Macro　F-Measure,　Micro　F-Measure,　Hierarchical　F-Measure　and　Lowest　Common　Ancestor　F-measure.　We　also　intuitively　show　how　MeSHProbeNet　is　able　to　extract　comprehensive　biomedical　knowledge　from　an　input　article.