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Evidence record 17922 · automatically gathered

H2AL: Hyperbolic Hierarchy-aware Aggregative Learning for Registration-based Few-shot Medical Image Segmentation

Registration-based Few-shot medical image segmentation (RFMIS) aims to generate pseudo-labels for unlabeled images by warping a labeled image through registration. However, existing methods primarily perform pixel-level optimization and inference in Euclidean space, treating anatomical structures as flat and disjoint. This neglect of inherent hierarchies degrades pseudo-label quality and weakens the discrimination of ambiguous regions, limiting the segmentation performance. To overcome this chal

Record details

Published: 7 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Bias & fairness · Healthcare
Retrieved: 10 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Healthcare

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Shared topic: Bias & fairness · Shared topic: Healthcare

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Shared topic: Bias & fairness · Shared topic: Healthcare

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Shared topic: Bias & fairness · Shared topic: Healthcare

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Shared topic: Bias & fairness · Shared topic: Healthcare

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Shared topic: Bias & fairness · Shared topic: Healthcare

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How to cite this record

ethics.ai (7 August 2026), “H2AL: Hyperbolic Hierarchy-aware Aggregative Learning for Registration-based Few-shot Medical Image Segmentation,” evidence record 17922, https://ethics.ai/record/17922 (originally published by arXiv cs.AI).

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