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Gan Gao

Publications and source records attributed to Gan Gao.

7 recordsLinked to original sources
arXiv2026

Extracting and analyzing 3D histomorphometric features related to perineural and lymphovascular invasion in prostate cancer

Diagnostic grading of prostate cancer (PCa) relies on the examination of 2D histology sections. However, the limited sampling of specimens afforded by 2D histopathology, and ambiguities when viewing 2D cross-sections, can lead to suboptimal treatment decisions. Recent studies have shown that 3D histomorphometric analysis of glands and nuclei can improve PCa risk assessment compared to analogous 2D features. Here, we expand on these efforts by developing an analytical pipeline to extract 3D features related to perineural invasion (PNI) and lymphovascular invasion (LVI), which correlate with poor prognosis for a variety of cancers. A 3D segmentation model (nnU-Net) was trained to segment nerves and vessels in 3D datasets of archived prostatectomy specimens that were optically cleared, labeled with a fluorescent analog of H&E, and imaged with open-top light-sheet (OTLS) microscopy. PNI- and LVI-related features, including metrics describing cancer-nerve and cancer-vessel proximity, were then extracted based on the 3D nerve/vessel segmentation masks in conjunction with 3D masks of cancer-enriched regions. As a preliminary exploration of the prognostic value of these features, we trained a supervised machine learning classifier to predict 5-year biochemical recurrence (BCR) outcomes, finding that 3D PNI-related features are moderately prognostic and outperform 2D PNI-related features (AUC = 0.71 vs. 0.52). Source code is available at https://github.com/sarahrahsl/SegCIA.git.

cs.CV↗
arXiv2024

Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments

Accurate patient diagnoses based on human tissue biopsies are hindered by current clinical practice, where pathologists assess only a limited number of thin 2D tissue slices sectioned from 3D volumetric tissue. Recent advances in non-destructive 3D pathology, such as open-top light-sheet microscopy, enable comprehensive imaging of spatially heterogeneous tissue morphologies, offering the feasibility to improve diagnostic determinations. A potential early route towards clinical adoption for 3D pathology is to rely on pathologists for final diagnosis based on viewing familiar 2D H&E-like image sections from the 3D datasets. However, manual examination of the massive 3D pathology datasets is infeasible. To address this, we present CARP3D, a deep learning triage approach that automatically identifies the highest-risk 2D slices within 3D volumetric biopsy, enabling time-efficient review by pathologists. For a given slice in the biopsy, we estimate its risk by performing attention-based aggregation of 2D patches within each slice, followed by pooling of the neighboring slices to compute a context-aware 2.5D risk score. For prostate cancer risk stratification, CARP3D achieves an area under the curve (AUC) of 90.4% for triaging slices, outperforming methods relying on independent analysis of 2D sections (AUC=81.3%). These results suggest that integrating additional depth context enhances the model's discriminative capabilities. In conclusion, CARP3D has the potential to improve pathologist diagnosis via accurate triage of high-risk slices within large-volume 3D pathology datasets.

eess.IV↗
arXiv2019

Cryptanalysis and improvement of dynamic quantum secret sharing protocol based on two-particle transform of Bell states

In this paper [Chin. Phys. B 27 (2018) 080304], Du and Bao proposed a quantum secret sharing protocol based on two-particle transform of Bell states. We study the security of the proposed protocol and find that it is not secure, that is, the two dishonest agents, Bob and Zach, can collude to obtain Alice's secret messages without the help of the other agents. Finally, we give a possible improvement of the proposed protocol.

quant-ph↗
arXiv2016

Comment on "proactive quantum secret sharing"

In the paper [Quantum Inf. Process. 14, 4237-4244 (2015)], Qin and Dai proposed a proactive quantum secret sharing scheme. We study the security of the proposed scheme and find that it is not secure. In the distribution phase of the proposed scheme, two dishonest participants may collaborate to eavesdrop the secret of the dealer without introducing any error. Incidentally, a possible improvement of the distribution phase is given.

quant-ph↗
arXiv2011

Comment on "Cryptanalysis and improvement of multiparty quantum secret sharing schemes"

We show that, using Wang et al. attack [T.-y. Wang, Q.-y. Wen, F. Gao, S. Lin, F.-c. Zhu, Phys. Lett. A 373 (2008) 65], the first agent and the last agent cannot eavesdrop all the secret messages in Zhang et al. QSSCM scheme [Z.-j. Zhang, G. Gao, X. Wang, L.-f Han, S.-h. Shi, Opt. Commun. 269 (2007) 418]. In some sense, Wang et al. attack is unsuccessful for Zhang et al. QSSCM scheme.

quant-ph↗
arXiv2011

Eavesdropping on the improved three-party quantum secret sharing protocol

Lin et al. [S. Lin, F. Gao, Q.-y. Wen, F.-c. Zhu, Opt. Commun. 281 (2008) 4553] pointed that the multiparty quantum secret sharing protocol [Z.-j. Zhang, G. Gao, X. Wang, L.-f. Han, S.-h. Shi, Opt. Commun. 269 (2007) 418] is insecure and proposed an improved three-party quantum secret sharing protocol. In this paper, we study the security of the improved three-party quantum secret sharing protocol and find that it is still insecure. Finally, a further improved three-party quantum secret sharing protocol is proposed.

quant-ph↗