{"_canonicalization":{"envelope_id":"axm_ + sha256(envelope minus {signature, axiom_id, anchors})","envelope_signature":"ed25519(envelope minus {signature, axiom_id})","json":"sort_keys=True, separators=(',',':'), ensure_ascii=False, allow_nan=False, utf-8","leaf_hash":"sha256(0x00 || canonical_json(envelope_full))","seal_signature":"ed25519(seal minus {signature, sig_algorithm})"},"axiom_id":"axm_7029b63a247949a0c293c537c6d203196440fbef13aa0cb6cfa1c9b3cd19c86c","bitcoin_anchor":{"bitcoin_attestations":[],"calendar_attestations":[],"ots_url":"","stamped_at":"","status":"pending_next_stamp"},"envelope":{"anchors":[{"chain":"crovia.axiom_graph","height":0,"merkle_proof":"spider_vendor_press_v1","root_at_anchor":"spider_vendor_press_v1"}],"axiom_id":"axm_7029b63a247949a0c293c537c6d203196440fbef13aa0cb6cfa1c9b3cd19c86c","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"86bde18f31d885f2e093ae58956f11418558d2944a75938947e8eec999f89b34","published":"Thu, 18 Jun 2026 00:00:00 -0400","receipt_hash":"86bde18f31d885f2e093ae58956f11418558d2944a75938947e8eec999f89b34","schema":"spider.news.vendor_press.v1","spider":"vendor_press","spider_record":{"axiom_subtype":"news.vendor_press.v1","category":"news","decision_hint":"POSITIVE","envelope_target":"AX.OBS","fingerprint":"86bde18f31d885f2e093ae58956f11418558d2944a75938947e8eec999f89b34","observed_at":"2026-06-18T04:43:37.219665Z","parent_run_hash":"de79a40f7b3537d88842f7ac355e799c5df2adcb4fc32a4e28096d4bbdf01739","published":"Thu, 18 Jun 2026 00:00:00 -0400","runtime_version":"0.1.0","schema":"spider.news.vendor_press.v1","source_status":200,"source_url":"https://export.arxiv.org/rss/cs.AI","spider":"vendor_press","summary_excerpt":"arXiv:2605.12567v2 Announce Type: replace-cross \nAbstract: The inherent electronic and speckle noise complicates clinical interpretation of ultrasound images. Conventional denoising methods rely on explicit noise assumptions whose validity diminishes under composite noise conditions. Learning-based methods are usually pretrained in a limited image domain using a labeled dataset, which implies inevitable domain shift in complex in vivo environments. This study proposes a Pyramid Self-Contrastive Learning (PSCL) framework for test-time ultrasound image denoising without pretraining. Given multiple noisy samples from only one-shot imaging, PSCL disentangles anatomical similarity and noise randomness into separate pyramid latent spaces. The clean image is then decoded from the anatomy space while discarding the noise space. We first apply PSCL to synthetic aperture ultrasound (SAU), where an Aperture-to-Aperture loop serves as a self-supervised proxy task to ensure denoising fidelity. Simu","title":"Pyramid Self-Contrastive Learning for Single-shot Test-time Ultrasound Image Denoising","url":"https://arxiv.org/abs/2605.12567","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.12567v2 Announce Type: replace-cross \nAbstract: The inherent electronic and speckle noise complicates clinical interpretation of ultrasound images. Conventional denoising methods rely on explicit noise assumptions whose validity diminishes under composite noise conditions. Learning-based methods are usually pretrained in a limited image domain using a labeled dataset, which implies inevitable domain shift in complex in vivo environments. This study proposes a Pyramid Self-Contrastive Learning (PSCL) framework for test-time ultrasound image denoising without pretraining. Given multiple noisy samples from only one-shot imaging, PSCL disentangles anatomical similarity and noise randomness into separate pyramid latent spaces. The clean image is then decoded from the anatomy space while discarding the noise space. We first apply PSCL to synthetic aperture ultrasound (SAU), where an Aperture-to-Aperture loop serves as a self-supervised proxy task to ensure denoising fidelity. Simu","title":"Pyramid Self-Contrastive Learning for Single-shot Test-time Ultrasound Image Denoising","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-06-18T04:43:37Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2605.12567"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:acb69a87b749cf3fc08306ac7b995c98307e6fcd34668cf0df60c24c10d1625d86389b3ff9c5e61677992a8252f272f5c3da3cba100d2cb2f8f1562457d53606","signer":"crovia.substrate","subject":{"observed_at":"2026-06-18T04:43:37Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2605.12567"},"tsa":{"authority":"crovia.substrate.bootstrap","rfc3161_token":"{\"kind\":\"crovia.bootstrap.tsa\",\"source_jsonl\":\"/opt/crovia/spider/data/news/vendor_press_v1.jsonl\",\"source_seal_merkle_root\":\"spider_vendor_press_v1\",\"upgrade_path\":\"Sessione H \\u2014 OpenTimestamps weekly anchor\"}"},"zk_mode":"clear","zk_proof":null},"ledger":{"leaf_hash":"7c51632527331165162be49f520fabb3f77524d20b5184ad76e9a2adbe15f836","leaf_index":233509,"ledger_path":"/opt/crovia/substrate/axiom_ledger.jsonl"},"merkle_proof":{"hash_alg":"sha256","leaf_prefix":"0x00","node_prefix":"0x01","odd_leaf_rule":"duplicate_last","path":[{"sibling":"d3b8e59dc25c6572aec158d014be6ba803f1eba64af2b2a6918d67e65835fb46","side":"left"},{"sibling":"f60fe35ec9f25ae139814203fbf3670b899daff453047c3c9dcb1b14f5a6147e","side":"right"},{"sibling":"6f11c2eda32f488a38f02f3b8fda24960fd7841dae37f13166a92fde45cf6912","side":"left"},{"sibling":"1ba02a06c10a51cf25d99ca1d85c63d9fcc0392b5ab9b376c64fb24a6a50938a","side":"right"},{"sibling":"ccef570cc5460ba72a01b2a0b3b6e7eee19dfe0476c303523c1d0b6d5e6c19ba","side":"right"},{"sibling":"c42f991fcbafbe53d8fc33e1c5c2d1d51ef70e0c2709828f03f40cba8d7b8389","side":"left"},{"sibling":"5e436eec5370aaf4cccb6c432bd004f0a554eab0e19ec87f70ba66470ad7303b","side":"right"},{"sibling":"fe9c643bdeb268143f61f15d89d0ff9dd03becb2914656c7a873d95f7266b5ce","side":"right"},{"sibling":"ad9e1cf26277141407aebd8692bfb16135dd1e614913e54b1dd445fed15d105a","side":"right"},{"sibling":"b151db1a7de0ce9a329250fae8b690f5b55ab3cfe5468a1fbb5f5bde0703b420","side":"right"},{"sibling":"7dc9143c057343b46a3b988492fba5262dec443cc1fb6070e3ea81543ca6a522","side":"right"},{"sibling":"ad5850946feb9a22361b5b9df0884f9ef1edcc7efea0374ff5782080ccb1a947","side":"right"},{"sibling":"410c633928fea11c5b4bdddb431956b1d7c320db9cda00d2fe32e0fcf888d7b7","side":"left"},{"sibling":"e616c34dbaf9456d5a6d3e2da82cde8621293c9f6d8a4cf9e441d7fd9cc81579","side":"right"},{"sibling":"94c0c932e61657f5e37fdba43f6ca9eddea8359425a7c1558dabe566911d5304","side":"right"},{"sibling":"a04392fb9f2a3a620840e3b3fecd93d12e6d224e481c162389d8ae64e7194599","side":"left"},{"sibling":"c300cf0154c136afc09b1702a0be98f4ba5b6dc5cf57e8cc714ec1eaf4196eff","side":"left"},{"sibling":"d841ad93efda0869e5eb97678f348f03f5caab4353e05ff4bf18f47fb945b822","side":"left"}]},"schema":"crovia.axiom_proof.v1","seal":{"first_collector_run_id":"","first_receipt_hash":"","jsonl_path":"/opt/crovia/substrate/axiom_ledger.jsonl","key_id":"430895f101d38164","last_collector_run_id":"","last_receipt_hash":"","leaf_count":234491,"merkle_root":"02576a6980e38bab47864ae2c57b5a5ff21e554e9bdf8f64bdf28155ff1aabec","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260618T143732Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-06-18T18:33:39Z","sig_algorithm":"ed25519","signature":"b6c708778fc38b7789a2b91156cfe87252a7cd3a1d29121cba11a0c78f8cf104ca3019fc50a962fa5a216bcc4922fc8f3f69c04d1f8332c6dc0d931e1012e502","signer_version":"1.1.0"},"trust_root":{"key_id":"430895f101d38164","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","signature_algorithm":"ed25519","url":"/registry/canon/TRUST_ROOT.md"},"verifier":{"spec":"/registry/canon/AXIOM_RECEIPT_v1.md","url":"/v/axm_7029b63a247949a0c293c537c6d203196440fbef13aa0cb6cfa1c9b3cd19c86c"}}