Biologically-inspired Semi-supervised Semantic Segmentation for Biomedical Imaging

• URL: http://arxiv.org/abs/2412.03192v2
• Date: Tue, 18 Mar 2025 14:28:57 GMT
• Title: Biologically-inspired Semi-supervised Semantic Segmentation for Biomedical Imaging
• Authors: Luca Ciampi, Gabriele Lagani, Giuseppe Amato, Fabrizio Falchi,
• Abstract summary: We propose a bio-inspired semi-supervised learning approach for training downsampling-upsampling semantic segmentation architectures.<n>We evaluate our methodology through experiments conducted on several widely used biomedical datasets.<n>Results show that our proposed method outperforms SOTA approaches across different levels of label availability.
• Score: 7.915123555266876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel bio-inspired semi-supervised learning approach for training downsampling-upsampling semantic segmentation architectures. The first stage does not use backpropagation. Rather, it exploits the Hebbian principle ``fire together, wire together'' as a local learning rule for updating the weights of both convolutional and transpose-convolutional layers, allowing unsupervised discovery of data features. In the second stage, the model is fine-tuned with standard backpropagation on a small subset of labeled data. We evaluate our methodology through experiments conducted on several widely used biomedical datasets, deeming that this domain is paramount in computer vision and is notably impacted by data scarcity. Results show that our proposed method outperforms SOTA approaches across different levels of label availability. Furthermore, we show that using our unsupervised stage to initialize the SOTA approaches leads to performance improvements. The code to replicate our experiments can be found at https://github.com/ciampluca/hebbian-bootstraping-semi-supervised-medical-imaging

Related papers

• Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training [7.915123555266876]
  Supervised deep learning for semantic segmentation has achieved excellent results in accurately identifying anatomical and pathological structures in medical images. It often requires large annotated training datasets, which limits its scalability in clinical settings. We introduce a novel semi-supervised teacher-student framework for biomedical image segmentation, inspired by the recent success of generative models.
  arXiv  Detail & Related papers  (2025-04-02T09:41:43Z)
• Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation [13.707121013895929]
  We present a novel semi-supervised learning method, Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation. We use distinct decoders for student and teacher networks while maintain the same encoder. To learn from unlabeled data, we create pseudo-labels generated by the teacher networks and augment the training data with the pseudo-labels.
  arXiv  Detail & Related papers  (2023-08-31T09:13:34Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• Hyperbolic Active Learning for Semantic Segmentation under Domain Shift [45.051035873942276]
  HALO sets a new state-of-the-art in active learning for semantic segmentation under domain shift. It is the first active learning approach that surpasses the performance of supervised domain adaptation while using only a small portion of labels.
  arXiv  Detail & Related papers  (2023-06-19T22:07:20Z)
• Universal Semi-Supervised Learning for Medical Image Classification [21.781201758182135]
  Semi-supervised learning (SSL) has attracted much attention since it reduces the expensive costs of collecting adequate well-labeled training data. Traditional SSL is built upon an assumption that labeled and unlabeled data should be from the same distribution. We propose a unified framework to leverage unseen unlabeled data for open-scenario semi-supervised medical image classification.
  arXiv  Detail & Related papers  (2023-04-08T16:12:36Z)
• 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 Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition [88.34182299496074]
  Action labels are only available on a source dataset, but unavailable on a target dataset in the training stage. We utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets. By segmenting and permuting temporal segments or human body parts, we design two self-supervised learning classification tasks.
  arXiv  Detail & Related papers  (2022-07-17T07:05:39Z)
• Deep learning based domain adaptation for mitochondria segmentation on EM volumes [5.682594415267948]
  We present three unsupervised domain adaptation strategies to improve mitochondria segmentation in the target domain. We propose a new training stopping criterion based on morphological priors obtained exclusively in the source domain. In the absence of validation labels, monitoring our proposed morphology-based metric is an intuitive and effective way to stop the training process and select in average optimal models.
  arXiv  Detail & Related papers  (2022-02-22T09:49:25Z)
• Weakly Supervised Change Detection Using Guided Anisotropic Difusion [97.43170678509478]
  We propose original ideas that help us to leverage such datasets in the context of change detection. First, we propose the guided anisotropic diffusion (GAD) algorithm, which improves semantic segmentation results. We then show its potential in two weakly-supervised learning strategies tailored for change detection.
  arXiv  Detail & Related papers  (2021-12-31T10:03:47Z)
• Semi-supervised Medical Image Segmentation through Dual-task Consistency [18.18484640332254]
  We propose a novel dual-task deep network that jointly predicts a pixel-wise segmentation map and a geometry-aware level set representation of the target. Our method can largely improve the performance by incorporating the unlabeled data. Our framework outperforms the state-of-the-art semi-supervised medical image segmentation methods.
  arXiv  Detail & Related papers  (2020-09-09T17:49:21Z)
• Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation [54.49894381464853]
  We propose to leverage both labeled and unlabeled data for instance segmentation with improved accuracy by knowledge distillation. We propose a novel Mask-guided Mean Teacher framework with Perturbation-sensitive Sample Mining. Experiments show that the proposed method improves the performance significantly compared with the supervised method learned from labeled data only.
  arXiv  Detail & Related papers  (2020-07-21T13:27:09Z)
• ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation [99.90263375737362]
  We propose ATSO, an asynchronous version of teacher-student optimization. ATSO partitions the unlabeled data into two subsets and alternately uses one subset to fine-tune the model and updates the label on the other subset. We evaluate ATSO on two popular medical image segmentation datasets and show its superior performance in various semi-supervised settings.
  arXiv  Detail & Related papers  (2020-06-24T04:05:12Z)
• MetricUNet: Synergistic Image- and Voxel-Level Learning for Precise CT Prostate Segmentation via Online Sampling [66.01558025094333]
  We propose a two-stage framework, with the first stage to quickly localize the prostate region and the second stage to precisely segment the prostate. We introduce a novel online metric learning module through voxel-wise sampling in the multi-task network. Our method can effectively learn more representative voxel-level features compared with the conventional learning methods with cross-entropy or Dice loss.
  arXiv  Detail & Related papers  (2020-05-15T10:37:02Z)

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.