18 citations · 27 across the 4 of their papers we have counts for
6 papers
ProtoPur: Prototype-Purity Pseudo-Labeling for Weakly Supervised Instance Learning in Pathology
Chen Shu, Chengyang Zhang, Boyu Fu +5
Weak bag-level labels are widely available in computational pathology, whereas reliable instance-level annotations for whole-slide images remain expensive and ambiguous. Weakly sup…
See, Measure, and Reason: Learning Visually Grounded Reasoning in Pathology
Chengyang Zhang, Wenchuan Zhang, Bo Li +8
Pathological assessment relies on recognizing fine-grained visual details in histological images. Vision-language models (VLMs) increasingly support pathology interpretation, yet t…
Do Pathology Vision-Language Models Truly See Pathology?
Chengyang Zhang, Wenchuan Zhang, Bo Li +10
Pathology vision-language models (VLMs) have recently progressed rapidly and are commonly evaluated by answer accuracy on pathology VQA benchmarks. However, we dig into current eva…
PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow
Chengyang Zhang, Wenchuan Zhang, Bo Li +5
Recent advances in Multimodal Large Language Models (MLLMs) and agent workflows have shown strong promise for computational pathology, yet reliable patch-level reasoning remains ch…
Validation of the Practicability of Logical Assessment Formula for Evaluations with Inaccurate Ground-Truth Labels: An Application Study on Tumour Segmentation for Breast Cancer
Yongquan Yang, Hong Bu
The logical assessment formula (LAF) is a new theory proposed for evaluations with inaccurate ground-truth labels (IAGTLs) to assess the predictive models for artificial intelligen…
One-Step Abductive Multi-Target Learning with Diverse Noisy Samples and Its Application to Tumour Segmentation for Breast Cancer
Yongquan Yang, Fengling Li, Yani Wei +4
Recent studies have demonstrated the effectiveness of the combination of machine learning and logical reasoning, including data-driven logical reasoning, knowledge driven machine l…