Pith Number

pith:PLZQWHKY

pith:2026:PLZQWHKYK3YKG4WTHYSBXGYJOA

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Adaptive Conformal Prediction for Reliable and Explainable Medical Image Classification

Lailil Muflikhah, Novanto Yudistira, One Octadion

Adaptive lambda criterion for RAPS guarantees at least 90 percent coverage in every difficulty stratum of medical images.

arxiv:2605.12917 v1 · 2026-05-13 · cs.CV · cs.LG

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Claims

C1strongest claim

We propose an Adaptive Lambda Criterion for RAPS that minimizes the worst-case coverage violation across prediction set size strata. ... our method achieves 95.72 percent global coverage with average set size 1.09 and at least 90 percent coverage across all strata.

C2weakest assumption

That defining strata by prediction set size effectively captures input difficulty levels and that optimizing lambda to minimize worst-case violation does not introduce new biases or reduce efficiency on unseen data distributions.

C3one line summary

An adaptive lambda criterion for RAPS achieves 95.72% global coverage and at least 90% coverage across all difficulty strata on medical image datasets while keeping average prediction set size at 1.09.

References

25 extracted · 25 resolved · 1 Pith anchors

[1] Scientific Data10(1), 41 (2023) 2023

[2] In: International Conference on Machine Learning, pp 2017

[3] A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification 2021 · arXiv:2107.07511

[4] In: Proceedings of the IEEE International Conference on Computer Vision, pp 2017

[5] 770–778 (2016) 2016

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:09:10.334299Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

7af30b1d5856f0a372d33e241b9b09701450053a27613408cec8ae88af5d0736

Aliases

arxiv: 2605.12917 · arxiv_version: 2605.12917v1 · doi: 10.48550/arxiv.2605.12917 · pith_short_12: PLZQWHKYK3YK · pith_short_16: PLZQWHKYK3YKG4WT · pith_short_8: PLZQWHKY

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/PLZQWHKYK3YKG4WTHYSBXGYJOA \
  | 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: 7af30b1d5856f0a372d33e241b9b09701450053a27613408cec8ae88af5d0736

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "98a38de11dbb3e3227e304097f92e46ec4091e97e44674cfe8965fbed1bd6e45",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-13T02:45:07Z",
    "title_canon_sha256": "9a7ab5548982d6148ded7ccb62937f794acbb4c11072decc994c79781d1b5e2e"
  },
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  "source": {
    "id": "2605.12917",
    "kind": "arxiv",
    "version": 1
  }
}