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.

Fetal MRI is often acquired with thick slices to mitigate motion artifacts, but this leads to partial volume effects and reduced through-plane spatial resolution, limiting precise anatomical analysis. To address this, various super-resolution methods have been proposed to reconstruct high-resolution volumes from thick-slice scans. Current methods face several major challenges: 1) relying on multi-stack paired data makes arbitrary super-resolution ratios difficult to achieve; 2) lacking robustness against voxel coordinate misalignment caused by partial volume effects; 3) failing to fully utilize the high in-plane resolution of MRI images. To address these issues, we propose a dual-consistency guided curriculum learning method based on implicit neural representation, which uses single-stack inputs to achieve arbitrary super-resolution. We introduce progressive consistency and volumetric consistency to mitigate voxel misalignment caused by partial volume effects and ensure smooth transitions during the model's curriculum-based training. Additionally, we design a curriculum-aware multi-scale feature interaction block to fully leverage thick-slice MRI's high in-plane resolution. Comprehensive evaluations on three fetal MRI datasets demonstrate SOTA performance, with particularly outstanding results in high-ratio super-resolution tasks.