{"cells":[{"cell_type":"markdown","metadata":{},"source":"# RSNA Knee Abnormality Detection: format-correct baseline\n\nThis is a **baseline**, not a competitive model. It establishes a valid\nsubmission pipeline for the code-competition rerun and nothing more: no image\nmodel is trained here, so the expected leaderboard score is the same macro-AUC\nas `sample_submission.csv` (0.5, since constant predictions carry no ranking\ninformation).\n\nStating that plainly matters, because a submission that *looks* like a model but\npredicts a constant is easy to mistake for a working one.\n\nWhat this notebook does do carefully:\n\n- discovers the competition directory rather than hardcoding a path, so it\n  survives the hidden-test swap at rerun time\n- reads `test.csv` (the study list), never the sample submission's row order\n- emits every one of the twelve target columns, in the exact required order\n- runs in seconds on CPU with internet disabled, satisfying the code\n  requirements\n\n## Where the real signal would come from\n\nThe dataset pairs each study with multi-sequence MRI and, at training time, the\noriginal radiology report. A serious solution reads the DICOM series --\n`test_series.csv` labels each series by anatomical plane, fluid sensitivity and\nfat suppression, which is exactly the metadata a 2.5D or multi-view CNN needs to\nroute sequences to the right encoder. The twelve targets are also strongly\ncorrelated (effusion with synovitis, medial OA with medial meniscus damage), so\na multi-label head with shared trunk beats twelve independent binary models."},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":"import os\nimport numpy as np\nimport pandas as pd\n\nTARGETS = [\"ACL\", \"MCL\", \"Medial Meniscus\", \"Lateral Meniscus\",\n           \"Medial OA\", \"Lateral OA\", \"PF OA\", \"Effusion\",\n           \"Synovitis\", \"Baker's\", \"Contusion\", \"Fracture\"]\n\ndef find_comp_dir():\n    \"\"\"Locate the directory holding test.csv without hardcoding the slug.\n\n    Kaggle may mount competition data directly under /kaggle/input/<slug>/ or\n    nested under /kaggle/input/competitions/<slug>/, and the layout can differ\n    between the interactive session and the rerun. Walk to find it.\n    \"\"\"\n    root = \"/kaggle/input\"\n    for dirpath, _dirnames, filenames in os.walk(root):\n        if \"test.csv\" in filenames:\n            return dirpath\n    listing = []\n    for dirpath, dirnames, filenames in os.walk(root):\n        listing.append(f\"{dirpath}: dirs={dirnames[:5]} files={filenames[:5]}\")\n        if len(listing) > 12:\n            break\n    raise FileNotFoundError(\"no test.csv under /kaggle/input\\n\" + \"\\n\".join(listing))\n\nCOMP = find_comp_dir()\nprint(\"competition dir:\", COMP)\nprint(\"contents:\", sorted(os.listdir(COMP))[:10])"},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":"test = pd.read_csv(os.path.join(COMP, \"test.csv\"))\nprint(\"test studies:\", len(test))\ntest.head()"},{"cell_type":"markdown","metadata":{},"source":"## Series metadata\n\nNot used for prediction here, but summarised so the rerun logs show what the\nhidden test set actually looks like -- useful when the constant baseline is\nlater replaced by a real model."},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":"series_path = os.path.join(COMP, \"test_series.csv\")\nif os.path.exists(series_path):\n    series = pd.read_csv(series_path)\n    print(\"series rows:\", len(series))\n    print(\"\\nseries per study:\")\n    print(series.groupby(\"StudyInstanceUID\").size().describe())\n    print(\"\\nanatomical planes:\")\n    print(series[\"Anatomical_Plane\"].value_counts())\n    print(\"\\nfluid sensitive / fat suppressed:\")\n    print(series[[\"Fluid_Sensitive\", \"Fat_Suppression\"]].mean())\nelse:\n    print(\"no test_series.csv found\")"},{"cell_type":"markdown","metadata":{},"source":"## Build the submission\n\nOne row per study from `test.csv`, twelve target columns in the required order.\nA constant 0.5 is used deliberately: with no trained model, any non-constant\nvalue would be an unvalidated guess, and under AUC a confident wrong ordering\nscores strictly worse than no ordering at all."},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":"sub = pd.DataFrame({\"StudyInstanceUID\": test[\"StudyInstanceUID\"].values})\nfor c in TARGETS:\n    sub[c] = 0.5\n\n# column order must match the sample submission exactly\nsub = sub[[\"StudyInstanceUID\"] + TARGETS]\n\nassert len(sub) == len(test), \"row count must match test.csv\"\nassert sub.isna().sum().sum() == 0, \"no NaNs allowed\"\nassert list(sub.columns) == [\"StudyInstanceUID\"] + TARGETS\n\nsub.to_csv(\"submission.csv\", index=False)\nprint(\"wrote submission.csv\", sub.shape)\nsub.head()"},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":"# read it back exactly as the scorer would\ncheck = pd.read_csv(\"submission.csv\")\nprint(check.dtypes)\nprint(\"\\nvalue range:\", check[TARGETS].values.min(), \"-\", check[TARGETS].values.max())\nprint(\"rows:\", len(check))\ncheck.head()"}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.11"}},"nbformat":4,"nbformat_minor":4}