{"url":"/sota/multiple-instance-learning-on-musk-v1","task":{"name":"Multiple Instance Learning","url":"/task/multiple-instance-learning","note":null},"dataset":{"name":"Musk v1","url":"/dataset/musk-v1"},"category":"Methodology","categories":["Methodology"],"category_note":null,"description":"**Multiple Instance Learning** is a type of weakly supervised learning algorithm where training data is arranged in bags, where each bag contains a set of instances $X=\\\\{x_1,x_2, \\ldots,x_M\\\\}$, and there is one single label $Y$ per bag, $Y\\in\\\\{0, 1\\\\}$ in the case of a binary classification problem. It is assumed that individual labels $y_1, y_2,\\ldots, y_M$ exist for the instances within a bag, but they are unknown during training. In the standard Multiple Instance assumption, a bag is considered negative if all its instances are negative. On the other hand, a bag is positive, if at least one instance in the bag is positive.\n\n\n<span class=\"description-source\">Source: [Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification ](https://arxiv.org/abs/1812.11560)</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["ACC","AUC"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"ACC":"higher","AUC":"higher"}},"counts":{"rows":2,"rows_with_code":2,"rows_with_paper_page":2,"rows_dated":2,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"Snuffy","metrics":{"ACC":"0.961","AUC":"0.989"},"uses_additional_data":false,"paper_date":"2024-08-15","paper":"/paper/snuffy-efficient-whole-slide-image-classifier","paper_url":"https://arxiv.org/abs/2408.08258v3","paper_title":"Snuffy: Efficient Whole Slide Image Classifier","code":"https://github.com/jafarinia/snuffy","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"DSMIL","metrics":{"ACC":"0.947"},"uses_additional_data":false,"paper_date":"2020-11-17","paper":"/paper/dual-stream-multiple-instance-learning","paper_url":"https://arxiv.org/abs/2011.08939v3","paper_title":"Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning","code":"https://github.com/binli123/dsmil-wsi","n_code_links":2,"syntology":{"n_ran":4,"n_unverified":2,"n_samples":6,"n_pointer_only_licence":0}}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":1,"rows_with_any_sample_ran":1,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":4,"n_unverified":2,"n_samples":6,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":4,"n_unverified":2,"n_samples":6,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}