← Back to all news
arXiv Computer Vision

Different Models, Shared Failures: Auditing Frozen-Encoder Pools for Medical Segmentation

The paper introduces CoFailMed, an audit framework that examines correlated failures among pretrained encoder pools used for medical image segmentation. By evaluating 11 public encoders across five 2D tasks with frozen features and 1×1 readouts, the study finds that models sharing checkpoints exhibit the highest per‑case Dice correlation, and that distinct checkpoints within the same encoder family are more correlated than cross‑family pairs. The analysis also shows that richer readouts and fine‑tuning can narrow but not eliminate these correlations, and that effective pool size at a 33rd‑percentile Dice threshold varies with encoder family and training recipe, underscoring the need to report absolute dependence and threshold‑specific effective size alongside accuracy.

Related stories

arXiv Computer Vision
Sep 11

Pre- and Post-Treatment Brain Metastases Segmentation Using nnU-Net with Post-Processing for BraTS 2026

The paper presents a segmentation pipeline for brain metastases in both pre‑ and post‑treatment cases using a 5‑fold nnU‑Net ResEnc‑L ensemble trained on 1,296 four‑modality cases. A rule‑based post‑processing cascade improves the lesion‑wise Dice similarity coefficient (LW‑DSC) for enhancing tumour, tumour core, whole tumour, and resection cavity sub‑regions, achieving LW‑DSC scores of 0.733, 0.751, 0.713, and 0.549 respectively on the official validation leaderboard. The authors conduct a five‑fold out‑of‑fold analysis to validate the robustness of each post‑processing stage, provide a mechanistic explanation of LW‑DSC behaviour, and report thirteen negative results that challenge common intuitions, with all code released under Apache‑2.0.

By Haobin Liu, Xin Wang
computer-visionbenchmarks
More like this →
Hugging Face Trending Papers
Sep 10

Pre- and Post-Treatment Brain Metastases Segmentation Using nnU-Net with Post-Processing for BraTS 2026

The paper presents a pragmatic segmentation pipeline for brain metastases in the BraTS 2026 Task 1, using a 5‑fold nnU-Net ResEnc‑L ensemble trained for 1,000 epochs on 1,296 four‑modality cases. A rule‑based post‑processing cascade tuned for the lesion‑wise Dice similarity coefficient (LW‑DSC) improves performance, achieving LW‑DSC scores of 0.733, 0.751, 0.713, and 0.549 on enhancing tumour, tumour core, whole tumour, and resection cavity, respectively. The authors audit each post‑processing stage with a five‑fold out‑of‑fold analysis, confirm two stages as robust, and provide a mechanistic analysis of LW‑DSC, along with thirteen negative results that challenge common intuitions.

computer-visionbenchmarks
More like this →
arXiv Computer Vision
Sep 25

BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation

The paper introduces BiCC, a bidirectional connected-component loss that pairs annotation- and prediction-derived partitions to score predicted components on their own scale. By deriving instances from predictions, BiCC directly penalizes false-positive components regardless of size, allowing a balance parameter to control the lesion-wise precision–recall trade-off. Across five datasets, BiCC outperforms existing instance-aware losses such as CC-DiceCE and blob loss in lesion-wise F1, and improves over DiceCE on multiple datasets.

By Luc Bouteille, Frederic Jonske, Jens Kleesiek, Alexander Jaus
computer-vision
More like this →
arXiv AI
Oct 27

Self-Supervised Pretraining for PHM: What Transfers, What Does Not, and What Federating It Costs

The study evaluates self‑supervised pretraining for Prognostics and Health Management (PHM) across 47 public datasets, using a compact Transformer encoder trained with masked patch prediction. Transfer performance is highly task‑dependent: it dramatically improves fault‑classification on CWRU and HSG18 but offers no reliable benefit for remaining‑useful‑life estimation on CMAPSS. The authors also compare centralized versus simulated federated pretraining, finding that centralized training generally outperforms federated training under matched budgets, and that the intrinsic SSL loss does not reliably predict downstream performance.

By Jos\'e Sanz-Dur\'an, David Sol\'is-Mart\'in
llms
More like this →
arXiv Computer Vision
6d ago

Readout and Operating-Point Sensitivity in Event-Stream Candidate Selection

The paper evaluates how readout settings and operating points affect event-stream candidate selection in event-triggered recording. Using 520 DailyDVS‑200 clips and 47 participants, the authors compare several encoder configurations—including DINO‑CLS, patch mean, and MobileNetV3‑Small—across different accumulation durations and view counts, reporting metrics such as target capture, false triggers, and partial AUC. The study finds that lightweight baselines and specific operating points significantly influence performance, while no clear advantage is observed for the JEPA‑specific method.

By Yifan Shuai
More like this →
arXiv Machine Learning
Jun 16

To forget is to preserve: Machine Unlearning for 3D medical image segmentation

arXiv:2606. 16180v1 Announce Type: cross Abstract: With new data privacy laws such as the General Data Protection Regulation (GDPR) [1] that allow individuals to ask that any of their personal information be erased from trained machine learning models, there has been a push to investigate the unlearning of data from models as a way to comply with these laws.

By Nitesh Kumar Singh, Akhilesh Singh, Arjun Arora
computer-visionsafety
More like this →