{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:AYHW4FJLDUX67FFNINFLDLNHRT","short_pith_number":"pith:AYHW4FJL","canonical_record":{"source":{"id":"1807.04459","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-12T08:08:31Z","cross_cats_sorted":[],"title_canon_sha256":"d7af876419f9b15edfef2516d0e73a498e4cb8105fd08277a4a077ba7b4cb121","abstract_canon_sha256":"69f86a337b70fd2c666b6d631adac3207b411d377923552d467c50b1a1ec3265"},"schema_version":"1.0"},"canonical_sha256":"060f6e152b1d2fef94ad434ab1ada78cfc86154947bee10898e7a03e013c2b7b","source":{"kind":"arxiv","id":"1807.04459","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.04459","created_at":"2026-05-18T00:03:15Z"},{"alias_kind":"arxiv_version","alias_value":"1807.04459v2","created_at":"2026-05-18T00:03:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.04459","created_at":"2026-05-18T00:03:15Z"},{"alias_kind":"pith_short_12","alias_value":"AYHW4FJLDUX6","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_16","alias_value":"AYHW4FJLDUX67FFN","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_8","alias_value":"AYHW4FJL","created_at":"2026-05-18T12:32:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:AYHW4FJLDUX67FFNINFLDLNHRT","target":"record","payload":{"canonical_record":{"source":{"id":"1807.04459","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-12T08:08:31Z","cross_cats_sorted":[],"title_canon_sha256":"d7af876419f9b15edfef2516d0e73a498e4cb8105fd08277a4a077ba7b4cb121","abstract_canon_sha256":"69f86a337b70fd2c666b6d631adac3207b411d377923552d467c50b1a1ec3265"},"schema_version":"1.0"},"canonical_sha256":"060f6e152b1d2fef94ad434ab1ada78cfc86154947bee10898e7a03e013c2b7b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:03:15.889265Z","signature_b64":"8izvzfFx50Nibwpe0Qq/gHr1OUgk9fmY6IZykl0IRJRGmKMMuROEzjkRH6qFM7f8DHXhAUDjNQX2Q7LjuFLABw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"060f6e152b1d2fef94ad434ab1ada78cfc86154947bee10898e7a03e013c2b7b","last_reissued_at":"2026-05-18T00:03:15.888652Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:03:15.888652Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.04459","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:03:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x/FCCutqShxdqJNvEweoTeuEMGPLtqfXid/XHr7ICi6YCQkurbBz/CLgXlUy1UBw15wY/u5vdc3Td7AD8FYxAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T12:21:17.964484Z"},"content_sha256":"62b0e1bfbd0121aa59f4d76fc98d9456bc033155ea4858eaef3bab113f8f5168","schema_version":"1.0","event_id":"sha256:62b0e1bfbd0121aa59f4d76fc98d9456bc033155ea4858eaef3bab113f8f5168"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:AYHW4FJLDUX67FFNINFLDLNHRT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Prostate Segmentation using 2D Bridged U-net","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongjian Shi, Junjun He, Wanli Chen, Xiaoying Tang, Yifan Chen, Yue Zhang, Yu Qiao","submitted_at":"2018-07-12T08:08:31Z","abstract_excerpt":"In this paper, we focus on three problems in deep learning based medical image segmentation. Firstly, U-net, as a popular model for medical image segmentation, is difficult to train when convolutional layers increase even though a deeper network usually has a better generalization ability because of more learnable parameters. Secondly, the exponential ReLU (ELU), as an alternative of ReLU, is not much different from ReLU when the network of interest gets deep. Thirdly, the Dice loss, as one of the pervasive loss functions for medical image segmentation, is not effective when the prediction is "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.04459","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:03:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W0eOReIhIc3hATMOajWlvyhlDhII97It8JgF+N5BgAZd65KiOVzaof/omhzsx5c8yXsr74QPUlu8l4qSO/4aDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T12:21:17.965111Z"},"content_sha256":"cf20f13813ef8252dbdc324f774a89b833b407a53d1b3973a15da2c4b26829dc","schema_version":"1.0","event_id":"sha256:cf20f13813ef8252dbdc324f774a89b833b407a53d1b3973a15da2c4b26829dc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AYHW4FJLDUX67FFNINFLDLNHRT/bundle.json","state_url":"https://pith.science/pith/AYHW4FJLDUX67FFNINFLDLNHRT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AYHW4FJLDUX67FFNINFLDLNHRT/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-25T12:21:17Z","links":{"resolver":"https://pith.science/pith/AYHW4FJLDUX67FFNINFLDLNHRT","bundle":"https://pith.science/pith/AYHW4FJLDUX67FFNINFLDLNHRT/bundle.json","state":"https://pith.science/pith/AYHW4FJLDUX67FFNINFLDLNHRT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AYHW4FJLDUX67FFNINFLDLNHRT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:AYHW4FJLDUX67FFNINFLDLNHRT","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"69f86a337b70fd2c666b6d631adac3207b411d377923552d467c50b1a1ec3265","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-12T08:08:31Z","title_canon_sha256":"d7af876419f9b15edfef2516d0e73a498e4cb8105fd08277a4a077ba7b4cb121"},"schema_version":"1.0","source":{"id":"1807.04459","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.04459","created_at":"2026-05-18T00:03:15Z"},{"alias_kind":"arxiv_version","alias_value":"1807.04459v2","created_at":"2026-05-18T00:03:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.04459","created_at":"2026-05-18T00:03:15Z"},{"alias_kind":"pith_short_12","alias_value":"AYHW4FJLDUX6","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_16","alias_value":"AYHW4FJLDUX67FFN","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_8","alias_value":"AYHW4FJL","created_at":"2026-05-18T12:32:13Z"}],"graph_snapshots":[{"event_id":"sha256:cf20f13813ef8252dbdc324f774a89b833b407a53d1b3973a15da2c4b26829dc","target":"graph","created_at":"2026-05-18T00:03:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"In this paper, we focus on three problems in deep learning based medical image segmentation. Firstly, U-net, as a popular model for medical image segmentation, is difficult to train when convolutional layers increase even though a deeper network usually has a better generalization ability because of more learnable parameters. Secondly, the exponential ReLU (ELU), as an alternative of ReLU, is not much different from ReLU when the network of interest gets deep. Thirdly, the Dice loss, as one of the pervasive loss functions for medical image segmentation, is not effective when the prediction is ","authors_text":"Hongjian Shi, Junjun He, Wanli Chen, Xiaoying Tang, Yifan Chen, Yue Zhang, Yu Qiao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-12T08:08:31Z","title":"Prostate Segmentation using 2D Bridged U-net"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.04459","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:62b0e1bfbd0121aa59f4d76fc98d9456bc033155ea4858eaef3bab113f8f5168","target":"record","created_at":"2026-05-18T00:03:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"69f86a337b70fd2c666b6d631adac3207b411d377923552d467c50b1a1ec3265","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-12T08:08:31Z","title_canon_sha256":"d7af876419f9b15edfef2516d0e73a498e4cb8105fd08277a4a077ba7b4cb121"},"schema_version":"1.0","source":{"id":"1807.04459","kind":"arxiv","version":2}},"canonical_sha256":"060f6e152b1d2fef94ad434ab1ada78cfc86154947bee10898e7a03e013c2b7b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"060f6e152b1d2fef94ad434ab1ada78cfc86154947bee10898e7a03e013c2b7b","first_computed_at":"2026-05-18T00:03:15.888652Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:03:15.888652Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8izvzfFx50Nibwpe0Qq/gHr1OUgk9fmY6IZykl0IRJRGmKMMuROEzjkRH6qFM7f8DHXhAUDjNQX2Q7LjuFLABw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:03:15.889265Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.04459","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:62b0e1bfbd0121aa59f4d76fc98d9456bc033155ea4858eaef3bab113f8f5168","sha256:cf20f13813ef8252dbdc324f774a89b833b407a53d1b3973a15da2c4b26829dc"],"state_sha256":"1a6a8a426e66a8abf03628fddd9c9b0cb900fce0651b4ee877400635858f1899"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MxQDmcTF0cWLLYhaVby620IrFTE2FiLiTANzynZjbekYnvpewL0jhlGBvRgFCmcOrEOcXYqYioFk5vgA72M1DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T12:21:17.968481Z","bundle_sha256":"e79c2ffdd10210e6fe9c0b2aa5788ed8d3dd51e45ec6dc0d31906d0b02953610"}}