{"submission_id":"79a9d403-b4bd-46e9-bdf6-51898296a049","submission_revision_id":"3b8c2c59-f0ac-4c94-b212-b8e1c3db54b0","revision_version":4,"submission_status":"under_review","submitted_at":"2026-08-15T22:44:05.434241Z","submitter_name":"Yongshuo Zong","submitter_email":"yongshuo.zong@gmail.com","task":{"status":"submitted","version":4,"extra_fields":{"difficulty_self_test":{"model":"Gemini Flash 3.6","estimated_score":"5–10%"}},"submitted_at":"2026-08-15T22:44:05.434241+00:00","industry_domain":"Clinical Diagnostic & Imaging","task_short_name":"pathwsi_navigation_instance_1","operating_system":"Linux","task_description":"Objective: Examine a digitized whole-slide image (H&E stain) of a sentinel lymph node biopsy from a breast cancer patient. Using the OpenSlide Python library, navigate and analyze the gigapixel image to identify all tissue regions containing metastatic tumor cells.\n\nClinical Context: In breast cancer surgery, sentinel lymph nodes are biopsied and examined for metastases. The pathological N-stage directly impacts treatment decisions. Metastases are classified as isolated tumor cells (ITC, < 0.2 mm), micrometastasis (0.2–2.0 mm), or macrometastasis (> 2.0 mm). The agent must examine the tissue, distinguish tumor from normal lymphoid tissue, and report affected regions.\n\nDesign Rationale: This task tests multimodal visual reasoning at scale. The agent is expected to write code that extracts tile images from the WSI via OpenSlide, then visually inspect those rendered tile images to classify tissue as tumor or non-tumor — mirroring the coarse-to-fine workflow of a human pathologist. The challenge is not in building a machine learning classifier (the agent must not train or fine-tune models), but in orchestrating an efficient multi-resolution search strategy over a gigapixel image and applying visual judgment at each step. Frontier multimodal agents have demonstrated meaningful performance on this class of task through iterative tile inspection, achieving tile-level F1 scores in the 0.50–0.85 range depending on tumor morphology and search strategy quality.\n\nStarting State: A working directory containing the raw whole-slide image and all required metadata:\n\n- input/slide.tif — Whole-slide image in TIFF format (multi-resolution pyramid)\n- input/slide_metadata.json — Slide metadata: slide_id, tissue type, stain, microns_per_pixel, analysis_tile_size (512 px at level 0), analysis_level (level used for grid evaluation)\n- output/ — Empty output directory\n- task_prompt.txt — Instructions\n- software/main.py — Evaluation script\n- Python 3 with pip available. The system-level OpenSlide library (libopenslide-dev, openslide-tools) is pre-installed. Agent should install openslide-python via pip.\n\nInstructions & Rules:\n\n- Open the slide with OpenSlide and inspect its pyramid structure \n- Generate a low-resolution overview thumbnail to understand tissue layout\n- Identify tissue-containing regions vs. empty glass background\n- Develop a systematic scanning strategy \n- Examine tissue regions at progressively higher magnification to detect metastatic tumor cells\n= Record all analysis grid tiles identified as containing tumor\n- Compute tumor area fraction (tumor tiles / tissue tiles)\n- Write outputs to output/: predictions.json\nThe analysis grid divides the slide into non-overlapping 256×256 pixel tiles at level 0. The grid has 377 columns (x: 0–376) and 770 rows (y: 0–769). Grid tile (x, y) covers level-0 pixels [x×256, (x+1)×256) × [y×256, (y+1)×256). No pre-built tumor detection function or trained classifier is provided — the agent writes its own tile extraction code using the OpenSlide API and uses its visual capabilities to assess each tile. The agent must not train, fine-tune, or download any machine learning models. Internet access is allowed for installing packages only.\n\n\nDataset: CAMELYON17 — Cancer Metastases in Lymph Nodes Challenge 2017 License: CC0 (Public Domain) \nSlide: patient_004_node_4.tif (centre_0, tumor-positive) \nPublication: Bándi et al. \"From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level.\" IEEE TMI 38(2), 550–560 (2019).\n\nData Source & File Mapping\nAll data comes from the CAMELYON17 training set on AWS S3 (public, no credentials needed):\n\ninput/slide.tif — The WSI file (3.25 GB). Source: s3://camelyon-dataset/CAMELYON17/images/patient_004_node_4.tif\ninput/slide_metadata.json — Contains slide dimensions, pixel spacing, grid parameters. Agent reads this to configure its analysis grid. \nreference/reference_annotations.json — Hidden ground-truth tile labels (supplied, verifier-only). Generated from the source annotation XML by rasterizing expert contours onto the analysis grid. Contains 6 tumor tiles out of 198,019 tissue tiles.\nreference/annotation.xml — Source CAMELYON17 lesion annotation XML (supplied for provenance). Origin: s3://camelyon-dataset/CAMELYON17/annotations/patient_004_node_4.xml\n\npathwsi_navigation_instance_1/\n├── task_prompt.txt              # Agent instructions\n├── prepare_data.py              # Run this to generate metadata + reference\n├── input/\n│   ├── slide.tif                # CAMELYON17 WSI (download from AWS)\n│   └── slide_metadata.json      # Generated by prepare_data.py\n├── output/\n│   └── .gitkeep                 # Agent writes predictions.json here\n├── reference/\n│   └── reference_annotations.json  # Generated by prepare_data.py (hidden from agent)\n└── software/\n    └── main.py                  # Evaluation script\n","software_licensing":"Free/Open Source","evaluation_criteria":null,"verification_method":"Compare the agent's output/predictions.json against hidden reference annotations using software/main.py. Scoring is a weighted combination of three components. All are fully deterministic and verifiable.\n\nCompare the agent's output/predictions.json against hidden reference/reference_annotations.json using software/main.py. Scoring is a weighted combination of three components, all fully deterministic and verifiable.\n\nTile-Level F1 (60% weight): Compute precision and recall of the agent's predicted tumor tiles against the reference tumor tiles derived from pathologist contour annotations. Only tiles within the tissue mask are considered — background tile predictions are ignored. F1 = 2 × precision × recall / (precision + recall). Full credit at F1 ≥ 0.80. Linear partial credit between F1 0.40 and 0.80, calculated as (F1 − 0.40) / 0.40. Zero credit below F1 0.40. If both prediction and reference are empty (no tumor), F1 is 1.0. The 0.80 threshold for full credit is calibrated to be achievable by strong multimodal agents using systematic visual search, while remaining challenging enough that naive strategies (e.g., selecting all tissue tiles or color-only heuristics) will fall well short.\n\nDetection Accuracy (20% weight): Binary classification — did the agent correctly determine whether the slide contains any metastasis? The agent's tumor_present field is compared to the reference. Score is 1.0 if the determination matches the ground truth. Score is 0.0 if wrong (false positive or false negative).\n\nTumor Area Fraction (20% weight): The agent's tumor_area_fraction (tumor_tile_count / total_tissue_tiles) is compared to the reference value. Full credit (1.0) if within ±0.02 absolute tolerance. Partial credit (0.5) if within ±0.05. Zero credit otherwise. The reference tumor_area_fraction is consistency-validated: it equals tumor_tile_count / total_tissue_tiles as computed from the reference tile sets, and both counts are recorded in the reference file.\n\nGround truth generation: Expert pathologist hand-drawn contour annotations (XML) from the CAMELYON17 challenge are converted to tile-level binary labels as follows. Each annotation contour polygon is rasterized onto a 64×64 sub-tile grid (via PIL.ImageDraw.polygon) for every tile whose bounding box intersects the contour. A tile is labeled tumor-positive if ≥ 20% of the sub-tile grid cells fall inside the contour (i.e., ≥ 819 of 4,096 rasterized pixels). This threshold matches HealthAgentBench (HAB) and is appropriate for this slide's small metastatic deposits (each annotation polygon spans ~300–500 level-0 pixels). Tissue tiles are identified by Otsu thresholding on a low-resolution grayscale overview of the slide at pyramid level 5; tiles confirmed as tumor-positive by annotation overlap are also included in the tissue set regardless of Otsu classification. The reference was generated by prepare_data.py using these exact parameters (TILE_SIZE=256, OVERLAP_THRESHOLD=0.2, raster_size=64).\n\nEvaluator validation: Before scoring, the evaluator validates the agent's output for schema correctness and internal consistency. It checks: (1) all required fields are present (slide_id, tumor_present, tumor_tiles, tumor_tile_count, total_tissue_tiles, tumor_area_fraction); (2) slide_id matches the reference; (3) tile coordinates are integers within the grid bounds (0–376, 0–769); (4) no duplicate tiles; (5) tumor_tile_count equals the actual number of unique tiles in the list; (6) tumor_area_fraction is consistent with tumor_tile_count / total_tissue_tiles. Validation errors (missing fields, non-list tumor_tiles) zero the score. Validation warnings (mismatched slide_id, out-of-bounds tiles, duplicates, count inconsistencies) are reported but do not affect scoring.\n\nSolver isolation: Since this task uses a slide and annotation from a public challenge dataset (CAMELYON17), the solver must run fully offline after initial dependency installation (pip install openslide-python). Internet access should be disabled or strictly allowlisted to package repositories only during the solver phase to prevent retrieval of the publicly available annotation XML.\n\nFinal Score = 0.60 × tile_f1_score + 0.20 × detection_score + 0.20 × area_fraction_score. Maximum = 1.0.","software_and_version":"Python, OpenSlide","specific_requirements":null,"expected_completion_time":null},"files":[{"file_id":"7dc18db1-81f4-4944-83a2-3f793641a7b0","filename":"patient_004_node_4.tif","category":"input_materials","content_type":"image/tiff","size_bytes":3252359079},{"file_id":"40ec8832-9a4a-4b5a-93ae-cf193608a454","filename":"task_prompt.txt","category":"input_materials","content_type":"text/plain","size_bytes":4316},{"file_id":"254b4952-9adc-44b6-9f71-1c512a0405e6","filename":"slide_metadata.json","category":"input_materials","content_type":"application/json","size_bytes":327},{"file_id":"734fa515-3b41-415b-9bd7-c0dddc660654","filename":"reference_annotations.json","category":"reference_output","content_type":"application/json","size_bytes":8621762},{"file_id":"660c64de-8959-42f4-b26b-8110ff3a17b6","filename":"main.py","category":"reference_output","content_type":"text/x-python-script","size_bytes":11231},{"file_id":"e44742a0-b868-46ac-9f77-3185cc572ace","filename":"prepare_data.py","category":"input_materials","content_type":"text/x-python-script","size_bytes":12764},{"file_id":"3dcc3c08-88a8-4403-acb4-1b5800a31d31","filename":"annotation.xml","category":"reference_output","content_type":"text/xml","size_bytes":2647}],"deep_review":{"overall_verdict":"Accept","review_text":"**Overall Verdict:** Accept — The submission is complete, coherent, objectively evaluable, and operationally reachable, with only optional evaluator-hardening improvements.\n\n**Completeness Check:**\n- Instruction: Complete — The task defines the clinical objective, navigation workflow, tile grid, restrictions, and required JSON output.\n- Inputs: Complete — The multiresolution WSI, slide metadata, task prompt, and preparation provenance are consistent and usable.\n- Toolchain: Complete — Linux, Python, OpenSlide, and routine open-source dependencies provide a practical single-machine workflow.\n- Reference output: Complete — The reference JSON is schema-valid and internally consistent, with six unique tumor tiles among 198,019 tissue tiles; the three source annotation polygons support those labels.\n- Rubric: Complete — The evaluator implements the documented F1, detection, and area-fraction components with the stated weights and thresholds.\n\n**File and Task Reachability:**\nAttachment classification: reachable. Task classification: reachable. The inspected files were `patient_004_node_4.tif`, `task_prompt.txt`, `slide_metadata.json`, `reference_annotations.json`, `main.py`, `prepare_data.py`, and `annotation.xml`. All small attachments were inspected completely. For the 3.25 GB WSI, bounded structural and payload inspection confirmed a valid little-endian BigTIFF, JPEG-compressed tiles, nine cleanly linked pyramid levels from 96,651×197,226 through 377×770, metadata-consistent dimensions, and an intact end-of-file structure. The bulk full-resolution tile payload was not exhaustively decoded, and the sampled low-resolution JPEG tiles could not be visually rendered in the review environment; this is a reviewer-tooling limitation rather than a submission barrier because the declared OpenSlide workflow supports the file. Solver-visible inputs are separated from the hidden annotations and evaluator. The end-to-end path is clear: inspect the WSI, emit `predictions.json`, and score it deterministically against the hidden reference. License keys, accounts, tokens, and other credentials are not required from the contributor. Confidential, proprietary, or internal material may be evaluated as submitted evidence without requiring additional disclosure.\n\n**Special Screening:**\n`public_benchmark` — The submission and inspected artifacts identify an exact slide and annotation from a named public challenge. The protected answer is potentially network-retrievable, but the specified offline solver phase with package-repository-only dependency access materially mitigates that risk. The public provenance and license claims were not independently verified because external lookup was outside the permitted evidence boundary.\n\n**Recommended Changes:**\nThe task and evidence are professionally assembled; no contributor revision is required. As optional hardening, make malformed coordinate types, out-of-bounds tiles, duplicates, count mismatches, and area-fraction inconsistencies fatal validation errors, or recompute scored values from validated fields.","reviewed_at":"2026-08-15T22:51:44.630189Z"},"claim":null}