Weiping Lin

Weiping Lin, Shen Liu, Runchen Zhu et al.

Pathology foundation models (PFMs), as large-scale pre-trained models tailored for computational pathology, have significantly advanced a wide range of applications. Their ability to leverage prior knowledge from massive datasets has streamlined the development of intelligent pathology models. However, we identify several critical and interrelated ethical risks that remain underexplored, yet must be addressed to enable the safe translation of PFMs from lab to clinic. These include the potential leakage of patient-sensitive attributes, disparities in model performance across demographic and institutional subgroups, and the reliance on diagnosis-irrelevant features that undermine clinical reliability. In this study, we pioneer the quantitative analysis for ethical risks in PFMs, including privacy leakage, clinical reliability, and group fairness. Specifically, we propose an evaluation framework that systematically measures key dimensions of ethical concern: the degree to which patient-sensitive attributes can be inferred from model representations, the extent of performance disparities across demographic and institutional subgroups, and the influence of diagnostically irrelevant features on model decisions. We further investigate the underlying causes of these ethical risks in PFMs and empirically validate our findings. Then we offer insights into potential directions for mitigating such risks, aiming to inform the development of more ethically robust PFMs. This work provides the first quantitative and systematic evaluation of ethical risks in PFMs. Our findings highlight the urgent need for ethical safeguards in PFMs and offer actionable insights for building more trustworthy and clinically robust PFMs. To facilitate future research and deployment, we will release the assessment framework as an online toolkit to support the development, auditing, and deployment of ethically robust PFMs.

Kai Zhang, Zhengzhong Yi, Shaojun Guo et al.

Fast, reliable decoders are pivotal components for enabling fault-tolerant quantum computation (FTQC). Neural network decoders like AlphaQubit have demonstrated potential, achieving higher accuracy than traditional human-designed decoding algorithms. However, existing implementations of neural network decoders lack the parallelism required to decode the syndrome stream generated by a superconducting logical qubit in real time. Moreover, integrating AlphaQubit with sliding window-based parallel decoding schemes presents non-trivial challenges: AlphaQubit is trained solely to output a single bit corresponding to the global logical correction for an entire memory experiment, rather than local physical corrections that can be easily integrated. We address this issue by training a recurrent, transformer-based neural network specifically tailored for parallel window decoding. While it still outputs a single bit, we derive training labels from a consistent set of local corrections and train on various types of decoding windows simultaneously. This approach enables the network to self-coordinate across neighboring windows, facilitating high-accuracy parallel decoding of arbitrarily long memory experiments. As a result, we overcome the throughput bottleneck that previously precluded the use of AlphaQubit-type decoders in FTQC. Our work presents the first scalable, neural-network-based parallel decoding framework that simultaneously achieves SOTA accuracy and the stringent throughput required for real-time quantum error correction. Using an end-to-end experimental workflow, we benchmark our decoder on the Zuchongzhi 3.2 superconducting quantum processor on surface codes with distances up to 7, demonstrating its superior accuracy. Moreover, we demonstrate that, using our approach, a single TPU v6e is capable of decoding surface codes with distances up to 25 within 1us per decoding round.

Bingjian Yao, Weiping Lin, Yan He et al.

The fine-grained annotations in whole slide images (WSIs) show the boundaries of various pathological regions. However, generating such detailed annotation is often costly, whereas the coarse annotations are relatively simpler to produce. Existing methods for refining coarse annotations often rely on extensive training samples or clean datasets, and fail to capture both intra-slide and inter-slide latent sematic patterns, limiting their precision. In this paper, we propose a prototype-guided approach. Specifically, we introduce a local-to-global approach to construct non-redundant representative prototypes by jointly modeling intra-slide local semantics and inter-slide contextual relationships. Then a prototype-guided pseudo-labeling module is proposed for refining coarse annotations. Finally, we employ dynamic data sampling and re-finetuning strategy to train a patch classifier. Extensive experiments on three publicly available WSI datasets, covering lymph, liver, and colorectal cancers, demonstrate that our method significantly outperforms existing state-of-the-art (SOTA) methods. The code will be available.