Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem

2019-11-05 · Sergey Pavlov, Alexey Artemov, Maksim Sharaev, Alexander Bernstein, Evgeny Burnaev

Segmentation of tumors in brain MRI images is a challenging task, where most recent methods demand large volumes of data with pixel-level annotations, which are generally costly to obtain. In contrast, image-level annotations, where only the presence of lesion is marked, are generally cheap, generated in far larger volumes compared to pixel-level labels, and contain less labeling noise. In the context of brain tumor segmentation, both pixel-level and image-level annotations are commonly available; thus, a natural question arises whether a segmentation procedure could take advantage of both. In the present work we: 1) propose a learning-based framework that allows simultaneous usage of both pixel- and image-level annotations in MRI images to learn a segmentation model for brain tumor; 2) study the influence of comparative amounts of pixel- and image-level annotations on the quality of brain tumor segmentation; 3) compare our approach to the traditional fully-supervised approach and show that the performance of our method in terms of segmentation quality may be competitive.

📄 PDF Abstract BibTeX arXiv:1911.01738

Code (1)

segis95/BRATS_Segmentation

Tasks

Brain Tumor Segmentation Segmentation Tumor Segmentation

Clinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Images

2026-04-22 · Joakim Nguyen, Jian Yu, Jinrui Fang, Nicholas Konz 외 arxiv

Accurate diagnosis of pediatric brain tumors, starting with histopathology, presents unique challenges for deep learning, including severe data scarcity, class imbalance, and fine-grained morphologic overlap across diagn…

Multiple Instance Learning Brain Tumor Classification Representation Learning Contrastive Learning

Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation

2019-11-05 · Zhanghexuan Ji, Yan Shen, Chunwei Ma, Mingchen Gao

The recent state-of-the-art deep learning methods have significantly improved brain tumor segmentation. However, fully supervised training requires a large amount of manually labeled masks, which is highly time-consuming…

Brain Tumor Segmentation Clustering Segmentation Tumor Segmentation+2

Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images

2022-09-20 · Jay J. Yoo, Khashayar Namdar, Farzad Khalvati

Training machine learning models to segment tumors and other anomalies in medical images is an important step for developing diagnostic tools but generally requires manually annotated ground truth segmentations, which ne…

Binary Classification Brain Tumor Segmentation Clustering Diagnostic+4

Brain Tumor Sequence Registration with Non-iterative Coarse-to-fine Networks and Dual Deep Supervision

2022-11-15 · Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim

In this study, we focus on brain tumor sequence registration between pre-operative and follow-up Magnetic Resonance Imaging (MRI) scans of brain glioma patients, in the context of Brain Tumor Sequence Registration challe…

TumorXAI: Self-Supervised Deep Learning Framework for Explainable Brain MRI Tumor Classification

2026-05-03 · Abrar Hossain Zahin, Amit Kumar Saha, Tanvir Mridha, Saifur Rahman 외 arxiv

Classifying brain tumors using magnetic resonance imaging (MRI) is crucial for early diagnosis and treatment; however, tumor heterogeneity and a dearth of annotated datasets restrict the use of supervised deep learning a…

Brain Tumor Classification Self-Supervised Learning