Pith Number

pith:NDIP27JO

pith:2020:NDIP27JOQ2GI5TEW5OSJGK6HCP

not attested not anchored not stored refs resolved

PathVQA: 30000+ Questions for Medical Visual Question Answering

Eric Xing, Luntian Mou, Pengtao Xie, Xuehai He, Yichen Zhang

The first pathology visual question answering dataset is created with 32,799 manually verified questions from 4,998 images.

arxiv:2003.10286 v1 · 2020-03-07 · cs.CL · cs.AI

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\pithnumber{NDIP27JOQ2GI5TEW5OSJGK6HCP}

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

To our best knowledge, this is the first dataset for pathology VQA.

C2weakest assumption

That questions automatically generated from image captions using NLP, followed by manual checks, produce medically accurate and representative questions that pathologists would actually ask when viewing the images.

C3one line summary

PathVQA is the first public dataset of over 32,000 questions on nearly 5,000 pathology images for medical visual question answering.

References

34 extracted · 34 resolved · 4 Pith anchors

[1] Vqa: Visual question answering 2015

[2] A multi-world approach to question answering about real-world scenes based on uncertain input 2014

[3] Image question answering: A visual semantic embedding model and a new dataset 2015

[4] Clevr: A diagnostic dataset for compositional language and elementary visual reasoning 2017

[5] Making the v in vqa matter: Elevating the role of image understanding in visual question answering 2017

Formal links

2 machine-checked theorem links

Cited by

44 papers in Pith

PathNavigate: A Training-Free Pathology Agent with Surprise-Guided Scan and Shared Slide Memory for Whole-Slide Image VQA

DDX-TRACE: A Benchmark for Medical Diagnostic Trajectories in VLMs

LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day

NVILA: Efficient Frontier Visual Language Models

FLARE: Fully Integration of Vision-Language Representations for Deep Cross-Modal Understanding

Receipt and verification

First computed	2026-05-17T23:38:53.418891Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

68d0fd7d2e868c8ecc96eba4932bc713fa6fcd7c8a770e0219c54df830b848ba

Aliases

arxiv: 2003.10286 · arxiv_version: 2003.10286v1 · doi: 10.48550/arxiv.2003.10286 · pith_short_12: NDIP27JOQ2GI · pith_short_16: NDIP27JOQ2GI5TEW · pith_short_8: NDIP27JO

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/NDIP27JOQ2GI5TEW5OSJGK6HCP \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 68d0fd7d2e868c8ecc96eba4932bc713fa6fcd7c8a770e0219c54df830b848ba

Canonical record JSON

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    "abstract_canon_sha256": "7d90650c41118c44b4714d4f5e346c41bc98b2570e8867fb21fd37f16b77d852",
    "cross_cats_sorted": [
      "cs.AI"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2020-03-07T17:55:41Z",
    "title_canon_sha256": "81632873afec56f9215e42e974206a080dc643bae35d6ca32526915cfdae1b6e"
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