# A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor Segmentation Source: [A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:08:39.928Z Authors: Ruitao Xie, Limai Jiang, Xiaoxi He, Yi Pan, Yunpeng Cai Published: 2024-08-02T11:18:32.000Z ## Abstract Machine-based brain tumor segmentation can help doctors make better diagnoses. However, the complex structure of brain tumors and expensive pixel-level annotations present challenges for automatic tumor segmentation. In this paper, we propose a counterfactual generation framework that not only achieves exceptional brain tumor segmentation performance without the need for pixel-level annotations, but also provides explainability. Our framework effectively separates class-related features from class-unrelated features of the samples, and generate new samples that preserve identity features while altering class attributes by embedding different class-related features. We perform topological data analysis on the extracted class-related features and obtain a globally explainable manifold, and for each abnormal sample to be segmented, a meaningful normal sample could be effectively generated with the guidance of the rule-based paths designed within the manifold for comparison for identifying the tumor regions. We evaluate our proposed method on two datasets, which demonstrates superior performance of brain tumor segmentation. The code is available at https://github.com/xrt11/tumor-segmentation. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Medical Anomaly Detection on BraTS 2023](): Medical Anomaly Detection; BraTS 2023; Dice Coefficient: 46.98. - [Medical Anomaly Detection on BraTS 2020](): Medical Anomaly Detection; BraTS 2020; Dice Score: 26.36. - [Medical Anomaly Detection on BraTS 2021](): Medical Anomaly Detection; BraTS 2021; Dice Score: 23.82. - [Medical Anomaly Detection on Medical Segmentation Decathlon (MSD)](): Medical Anomaly Detection; Medical Segmentation Decathlon (MSD); Dice Score (%): 27.96. ## Original sources and implementation [Original paper]() [PDF]()