Lele Cong

Bo Yu, Hechang Chen, Yunke Zhang et al.

Artificial intelligence-enabled histopathological data analysis has become a valuable assistant to the pathologist. However, existing models lack representation and inference abilities compared with those of pathologists, especially in cancer subtype diagnosis, which is unconvincing in clinical practice. For instance, pathologists typically observe the lesions of a slide from global to local, and then can give a diagnosis based on their knowledge and experience. In this paper, we propose a Data and Knowledge Co-driving (D&K) model to replicate the process of cancer subtype classification on a histopathological slide like a pathologist. Specifically, in the data-driven module, the bagging mechanism in ensemble learning is leveraged to integrate the histological features from various bags extracted by the embedding representation unit. Furthermore, a knowledge-driven module is established based on the Gestalt principle in psychology to build the three-dimensional (3D) expert knowledge space and map histological features into this space for metric. Then, the diagnosis can be made according to the Euclidean distance between them. Extensive experimental results on both public and in-house datasets demonstrate that the D&K model has a high performance and credible results compared with the state-of-the-art methods for diagnosing histopathological subtypes. Code: https://github.com/Dennis-YB/Data-and-Knowledge-Co-driving-for-Cancer-Subtypes-Classification

Bo Yu, Hechang Chen, Chengyou Jia et al.

Raman spectroscopy (RS) has been widely used for disease diagnosis, e.g., cardiovascular disease (CVD), owing to its efficiency and component-specific testing capabilities. A series of popular deep learning methods have recently been introduced to learn nuance features from RS for binary classifications and achieved outstanding performance than conventional machine learning methods. However, these existing deep learning methods still confront some challenges in classifying subtypes of CVD. For example, the nuance between subtypes is quite hard to capture and represent by intelligent models due to the chillingly similar shape of RS sequences. Moreover, medical history information is an essential resource for distinguishing subtypes, but they are underutilized. In light of this, we propose a multi-modality multi-scale model called M3S, which is a novel deep learning method with two core modules to address these issues. First, we convert RS data to various resolution images by the Gramian angular field (GAF) to enlarge nuance, and a two-branch structure is leveraged to get embeddings for distinction in the multi-scale feature extraction module. Second, a probability matrix and a weight matrix are used to enhance the classification capacity by combining the RS and medical history data in the multi-modality data fusion module. We perform extensive evaluations of M3S and found its outstanding performance on our in-house dataset, with accuracy, precision, recall, specificity, and F1 score of 0.9330, 0.9379, 0.9291, 0.9752, and 0.9334, respectively. These results demonstrate that the M3S has high performance and robustness compared with popular methods in diagnosing CVD subtypes.

Zesheng Hong, Yubiao Yue, Yubin Chen et al.

Computer-aided diagnostics has benefited from the development of deep learning-based computer vision techniques in these years. Traditional supervised deep learning methods assume that the test sample is drawn from the identical distribution as the training data. However, it is possible to encounter out-of-distribution samples in real-world clinical scenarios, which may cause silent failure in deep learning-based medical image analysis tasks. Recently, research has explored various out-of-distribution (OOD) detection situations and techniques to enable a trustworthy medical AI system. In this survey, we systematically review the recent advances in OOD detection in medical image analysis. We first explore several factors that may cause a distributional shift when using a deep-learning-based model in clinic scenarios, with three different types of distributional shift well defined on top of these factors. Then a framework is suggested to categorize and feature existing solutions, while the previous studies are reviewed based on the methodology taxonomy. Our discussion also includes evaluation protocols and metrics, as well as the challenge and a research direction lack of exploration.

Zheng Li, Jiayi Xu, Zhikai Hu et al.

Diagnosing hepatic diseases accurately and interpretably is critical, yet it remains challenging in real-world clinical settings. Existing AI approaches for clinical diagnosis often lack transparency, structured reasoning, and deployability. Recent efforts have leveraged large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent collaboration. However, these approaches typically retrieve evidence from a single source and fail to support iterative, role-specialized deliberation grounded in structured clinical data. To address this, we propose MedCoRAG (i.e., Medical Collaborative RAG), an end-to-end framework that generates diagnostic hypotheses from standardized abnormal findings and constructs a patient-specific evidence package by jointly retrieving and pruning UMLS knowledge graph paths and clinical guidelines. It then performs Multi-Agent Collaborative Reasoning: a Router Agent dynamically dispatches Specialist Agents based on case complexity; these agents iteratively reason over the evidence and trigger targeted re-retrievals when needed, while a Generalist Agent synthesizes all deliberations into a traceable consensus diagnosis that emulates multidisciplinary consultation. Experimental results on hepatic disease cases from MIMIC-IV show that MedCoRAG outperforms existing methods and closed-source models in both diagnostic performance and reasoning interpretability.