DualFete: Revisiting Teacher-Student Interactions from a Feedback Perspective for Semi-supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2511.09319v1
• Date: Thu, 13 Nov 2025 01:46:20 GMT
• Title: DualFete: Revisiting Teacher-Student Interactions from a Feedback Perspective for Semi-supervised Medical Image Segmentation
• Abstract summary: This work introduces a feedback mechanism into the teacher-student framework to counteract error reconfirmations.<n>We specify this interaction hinges on two key components: the feedback attributor, which designates pseudo-labels triggering the student's update, and the feedback receiver, which determines where to apply this feedback.<n>Building on this, a dual-teacher feedback model is further proposed, which allows more dynamics in the feedback loop and fosters more gains.
• Score: 12.629975999398303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The teacher-student paradigm has emerged as a canonical framework in semi-supervised learning. When applied to medical image segmentation, the paradigm faces challenges due to inherent image ambiguities, making it particularly vulnerable to erroneous supervision. Crucially, the student's iterative reconfirmation of these errors leads to self-reinforcing bias. While some studies attempt to mitigate this bias, they often rely on external modifications to the conventional teacher-student framework, overlooking its intrinsic potential for error correction. In response, this work introduces a feedback mechanism into the teacher-student framework to counteract error reconfirmations. Here, the student provides feedback on the changes induced by the teacher's pseudo-labels, enabling the teacher to refine these labels accordingly. We specify that this interaction hinges on two key components: the feedback attributor, which designates pseudo-labels triggering the student's update, and the feedback receiver, which determines where to apply this feedback. Building on this, a dual-teacher feedback model is further proposed, which allows more dynamics in the feedback loop and fosters more gains by resolving disagreements through cross-teacher supervision while avoiding consistent errors. Comprehensive evaluations on three medical image benchmarks demonstrate the method's effectiveness in addressing error propagation in semi-supervised medical image segmentation.

Related papers

• OPD-Aha: From Linguistic Momentum to Visual Reflection in Multimodal On-Policy Distillation [15.986808086256792]
  Privileged on-policy distillation improves multimodal reasoning by allowing a teacher to evaluate student trajectories using rich, training-only visual evidence.<n>We find that the teacher's visual corrective preference is not lost under this misleading agreement.<n>We introduce OPD-Aha, which reconstructs the distillation target directly from this isolated visual preference.
  arXiv  Detail & Related papers  (2026-09-15T00:25:28Z)
• VAD: Attributing Visual Evidence for Target Reconstruction in Multimodal On-Policy Distillation [46.347555076026964]
  Multimodal on-policy distillation transfers fine-grained visual knowledge by supervising student-generated trajectories with a privileged-view teacher.<n>Key challenge is to estimate which corrections are supported by visual evidence, not merely where or how strongly to distill.<n>We introduce Visual Attribution Distillation (VAD), a counterfactual target-reconstruction algorithm that estimates the visually attributable part of a teacher correction.
  arXiv  Detail & Related papers  (2026-07-30T17:43:46Z)
• Purified OPSD: On-Policy Self-Distillation Without Losing How to Think [52.879387744920415]
  We find that on-policy self-distillation (OPSD) consistently fails on long chain-of-thought (long-CoT) reasoning models.<n>We propose a two-step solution: first, we construct a reference-only teacher to isolate the non-transferable component of the supervision signal.<n>Second, we use pointwise mutual information (PMI) as the mechanism to transform this residual into a well-formed PMI target distribution.
  arXiv  Detail & Related papers  (2026-07-02T14:33:07Z)
• What Drives Interactive Improvement from Feedback? [0.7330217643497284]
  We study when natural-language feedback produces improvement beyond the gains obtainable from repeated attempts alone.<n>In multi-turn language agent setting, higher final accuracy can reflect useful feedback, but it can also arise from resampling, format correction, or additional test-time computation.<n>We introduce a controlled student-teacher protocol across Omni-MATH, Codeforces, BBEH Linguini, and ARC-AGI1.
  arXiv  Detail & Related papers  (2026-06-29T18:06:36Z)
• Scribble-Supervised Medical Image Segmentation with Dynamic Teacher Switching and Hierarchical Consistency [7.48591408877799]
  We propose SDT-Net, a novel dual-teacher, single-student framework to maximize supervision quality from weak signals.<n>Our method features a Dynamic Teacher Switching (DTS) module to adaptively select the most reliable teacher.<n>This selected teacher then guides the student via two synergistic mechanisms: high-confidence pseudo-labels, refined by a Pick Reliable Pixels (PRP) mechanism, and multi-level feature alignment.
  arXiv  Detail & Related papers  (2026-01-21T01:01:01Z)
• "The Whole Is Greater Than the Sum of Its Parts": A Compatibility-Aware Multi-Teacher CoT Distillation Framework [16.96094045628127]
  Chain-of-Thought (CoT) reasoning empowers Large Language Models (LLMs) with remarkable capabilities but typically requires prohibitive parameter scales.<n>CoT distillation has emerged as a promising paradigm to transfer reasoning prowess into compact Student Models (SLMs)<n>We introduce COMPACT, a framework that adaptively fuses supervisions from different teachers by dynamically weighting teacher gradients.
  arXiv  Detail & Related papers  (2026-01-20T14:05:19Z)
• Adaptive Knowledge Transferring with Switching Dual-Student Framework for Semi-Supervised Medical Image Segmentation [11.625763201387771]
  Teacher-student frameworks have emerged as a leading approach in semi-supervised medical image segmentation.<n>We introduce a novel Dual-Student architecture that strategically selects the most reliable student at each iteration.<n>We also introduce a strategy of Loss-Aware Exponential Moving Average to dynamically ensure that the teacher absorbs meaningful information from students.
  arXiv  Detail & Related papers  (2025-10-28T12:42:33Z)
• RoboView-Bias: Benchmarking Visual Bias in Embodied Agents for Robotic Manipulation [67.38036090822982]
  We propose RoboView-Bias, the first benchmark specifically designed to quantify visual bias in robotic manipulation.<n>We create 2,127 task instances that enable robust measurement of biases induced by individual visual factors and their interactions.<n>Our results highlight that systematic analysis of visual bias is a prerequisite for developing safe and reliable general-purpose embodied agents.
  arXiv  Detail & Related papers  (2025-09-26T13:53:25Z)
• Learning to Focus: Causal Attention Distillation via Gradient-Guided Token Pruning [62.23671919314693]
  Large language models (LLMs) have demonstrated significant improvements in contextual understanding.<n>However, their ability to attend to truly critical information during long-context reasoning and generation still falls behind the pace.<n>We introduce a two-stage framework called Learning to Focus (LeaF) to mitigate confounding factors.
  arXiv  Detail & Related papers  (2025-06-09T15:16:39Z)
• Dual-Teacher Ensemble Models with Double-Copy-Paste for 3D Semi-Supervised Medical Image Segmentation [31.460549289419923]
  Semi-supervised learning (SSL) techniques address the high labeling costs in 3D medical image segmentation. We introduce the Staged Selective Ensemble (SSE) module, which selects different ensemble methods based on the characteristics of the samples. Experimental results demonstrate the effectiveness of our proposed method in 3D medical image segmentation tasks.
  arXiv  Detail & Related papers  (2024-10-15T11:23:15Z)
• Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors [78.53699244846285]
  Large language models (LLMs) present an opportunity to scale high-quality personalized education to all. LLMs struggle to precisely detect student's errors and tailor their feedback to these errors. Inspired by real-world teaching practice where teachers identify student errors and customize their response based on them, we focus on verifying student solutions.
  arXiv  Detail & Related papers  (2024-07-12T10:11:40Z)
• Alternate Diverse Teaching for Semi-supervised Medical Image Segmentation [62.021828104757745]
  We propose AD-MT, an alternate diverse teaching approach in a teacher-student framework. It involves a single student model and two non-trainable teacher models that are momentum-updated periodically and randomly in an alternate fashion.
  arXiv  Detail & Related papers  (2023-11-29T02:44:54Z)
• Improving the Robustness of Distantly-Supervised Named Entity Recognition via Uncertainty-Aware Teacher Learning and Student-Student Collaborative Learning [24.733773208117363]
  We propose Uncertainty-Aware Teacher Learning to reduce the number of incorrect pseudo labels in the self-training stage.<n>We also propose Student-Student Collaborative Learning that allows the transfer of reliable labels between two student networks.<n>We evaluate our proposed method on five DS-NER datasets, demonstrating that our method is superior to the state-of-the-art DS-NER methods.
  arXiv  Detail & Related papers  (2023-11-14T09:09:58Z)
• Switching Temporary Teachers for Semi-Supervised Semantic Segmentation [45.20519672287495]
  The teacher-student framework, prevalent in semi-supervised semantic segmentation, mainly employs the exponential moving average (EMA) to update a single teacher's weights based on the student's. This paper introduces Dual Teacher, a simple yet effective approach that employs dual temporary teachers aiming to alleviate the coupling problem for the student.
  arXiv  Detail & Related papers  (2023-10-28T08:49:16Z)
• A denoised Mean Teacher for domain adaptive point cloud registration [81.43344461130474]
  Point cloud-based medical registration promises increased computational efficiency, robustness to intensity shifts, and anonymity preservation. Supervised training on synthetic deformations is an alternative but, in turn, suffers from the domain gap to the real domain. We present a denoised teacher-student paradigm for point cloud registration, comprising two complementary denoising strategies.
  arXiv  Detail & Related papers  (2023-06-26T15:03:47Z)
• On student-teacher deviations in distillation: does it pay to disobey? [54.908344098305804]
  Knowledge distillation has been widely used to improve the test accuracy of a "student" network. Despite being trained to fit the teacher's probabilities, the student may not only significantly deviate from the teacher probabilities, but may also outdo the teacher in performance.
  arXiv  Detail & Related papers  (2023-01-30T14:25:02Z)
• Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble [56.705249154629264]
  Self-training teacher-student frameworks are proposed to improve the robustness of NER models. In this paper, we propose an adaptive teacher learning comprised of two teacher-student networks. Fine-grained student ensemble updates each fragment of the teacher model with a temporal moving average of the corresponding fragment of the student, which enhances consistent predictions on each model fragment against noise.
  arXiv  Detail & Related papers  (2022-12-13T12:14:09Z)

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.