Promptable cancer segmentation using minimal expert-curated data

• URL: http://arxiv.org/abs/2505.17915v1
• Date: Fri, 23 May 2025 13:56:40 GMT
• Title: Promptable cancer segmentation using minimal expert-curated data
• Authors: Lynn Karam, Yipei Wang, Veeru Kasivisvanathan, Mirabela Rusu, Yipeng Hu, Shaheer U. Saeed,
• Abstract summary: Automated segmentation of cancer on medical images can aid targeted diagnostic and therapeutic procedures.<n>Its adoption is limited by the high cost of expert annotations required for training and inter-observer variability in datasets.<n>We propose a novel approach for promptable segmentation requiring only 24 fully-segmented images, supplemented by 8 weakly-labelled images.
• Score: 5.097733221827974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated segmentation of cancer on medical images can aid targeted diagnostic and therapeutic procedures. However, its adoption is limited by the high cost of expert annotations required for training and inter-observer variability in datasets. While weakly-supervised methods mitigate some challenges, using binary histology labels for training as opposed to requiring full segmentation, they require large paired datasets of histology and images, which are difficult to curate. Similarly, promptable segmentation aims to allow segmentation with no re-training for new tasks at inference, however, existing models perform poorly on pathological regions, again necessitating large datasets for training. In this work we propose a novel approach for promptable segmentation requiring only 24 fully-segmented images, supplemented by 8 weakly-labelled images, for training. Curating this minimal data to a high standard is relatively feasible and thus issues with the cost and variability of obtaining labels can be mitigated. By leveraging two classifiers, one weakly-supervised and one fully-supervised, our method refines segmentation through a guided search process initiated by a single-point prompt. Our approach outperforms existing promptable segmentation methods, and performs comparably with fully-supervised methods, for the task of prostate cancer segmentation, while using substantially less annotated data (up to 100X less). This enables promptable segmentation with very minimal labelled data, such that the labels can be curated to a very high standard.

Related papers

• SP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation [10.127428696255848]
  SuperPixel-Propagated Pseudo-label learning method is proposed to handle the inadequate supervisory information challenge in weakly semi-supervised segmentation. Our method achieves state-of-the-art performance on both tumor and organ segmentation datasets under the WSSS setting.
  arXiv  Detail & Related papers  (2024-11-18T15:14:36Z)
• Guidelines for Cerebrovascular Segmentation: Managing Imperfect Annotations in the context of Semi-Supervised Learning [3.231698506153459]
  Supervised learning methods achieve excellent performances when fed with a sufficient amount of labeled data. Such labels are typically highly time-consuming, error-prone and expensive to produce. Semi-supervised learning approaches leverage both labeled and unlabeled data, and are very useful when only a small fraction of the dataset is labeled.
  arXiv  Detail & Related papers  (2024-04-02T09:31:06Z)
• Explainable Semantic Medical Image Segmentation with Style [7.074258860680265]
  We propose a fully supervised generative framework that can achieve generalisable segmentation with only limited labelled data. The proposed approach creates medical image style paired with a segmentation task driven discriminator incorporating end-to-end adversarial training. Experiments on a fully semantic, publicly available pelvis dataset demonstrated that our method is more generalisable to shifts than other state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-10T04:34:51Z)
• An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning [58.59343434538218]
  We propose a simple but quite effective approach to predict accurate negative pseudo-labels of unlabeled data from an indirect learning perspective. Our approach can be implemented in just few lines of code by only using off-the-shelf operations.
  arXiv  Detail & Related papers  (2022-09-28T02:11:34Z)
• PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training [52.895952593202054]
  We propose a new semi-supervised adversarial method called Patch Confidence Adrial Training (PCA) for medical image segmentation. PCA learns the pixel structure and context information in each patch to get enough gradient feedback, which aids the discriminator in convergent to an optimal state. Our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.
  arXiv  Detail & Related papers  (2022-07-24T07:45:47Z)
• Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift [68.6874404805223]
  We propose several strategies for learning from partially overlapping labels in the context of abdominal organ segmentation. We find that combining a semi-supervised approach with an adaptive cross entropy loss can successfully exploit heterogeneously annotated data.
  arXiv  Detail & Related papers  (2021-07-13T09:22:24Z)
• Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentation [85.0078917060652]
  We propose a semi-weakly supervised segmentation algorithm to overcome this barrier. Our approach is based on a new formulation of deep supervision and student-teacher model. With our novel training regime for segmentation that flexibly makes use of images that are either fully labeled, marked with bounding boxes, just global labels, or not at all, we are able to cut the requirement for expensive labels by 94.22%.
  arXiv  Detail & Related papers  (2021-04-27T14:51:19Z)
• A Closer Look at Self-training for Zero-Label Semantic Segmentation [53.4488444382874]
  Being able to segment unseen classes not observed during training is an important technical challenge in deep learning. Prior zero-label semantic segmentation works approach this task by learning visual-semantic embeddings or generative models. We propose a consistency regularizer to filter out noisy pseudo-labels by taking the intersections of the pseudo-labels generated from different augmentations of the same image.
  arXiv  Detail & Related papers  (2021-04-21T14:34:33Z)
• Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data [123.03252888189546]
  We propose Vicinal Labels Under Uncertainty (VLUU) to bridge the methodological gaps in partially supervised learning (PSL) under data scarcity. Motivated by multi-task learning and vicinal risk minimization, VLUU transforms the partially supervised problem into a fully supervised problem by generating vicinal labels. Our research suggests a new research direction in label-efficient deep learning with partial supervision.
  arXiv  Detail & Related papers  (2020-11-28T16:31:00Z)
• Train, Learn, Expand, Repeat [0.15833270109954134]
  High-quality labeled data is essential to successfully train supervised machine learning models. Medical professionals who can expertly label the data are a scarce and expensive resource. We apply this technique on the segmentation of intracranial hemorrhage (ICH) in CT scans of the brain.
  arXiv  Detail & Related papers  (2020-03-18T20:55:38Z)
• Semi-supervised few-shot learning for medical image segmentation [21.349705243254423]
  Recent attempts to alleviate the need for large annotated datasets have developed training strategies under the few-shot learning paradigm. We propose a novel few-shot learning framework for semantic segmentation, where unlabeled images are also made available at each episode. We show that including unlabeled surrogate tasks in the episodic training leads to more powerful feature representations.
  arXiv  Detail & Related papers  (2020-03-18T20:37:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.