Hugging Face Trending Papers
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.
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
arXiv:2609.24769v1 Announce Type: new
Abstract: Brain metastases are the most common intracranial malignancy, occurring in roughly 30% of patients with primary solid tumors and carrying a median surv...
By Mahdi Islam, Musarrat Tabassum
arXiv:2607. 22749v1 Announce Type: cross Abstract: Tracking residual tumor after surgery is essential for catching recurrence early, but automating post-operative glioma segmentation remains a difficult task.
By Alexandru Cri\c{s}an, Diana Borza
The paper introduces NeuroFusion, an assistive brain‑MRI report generator that surfaces latent tumor signals from a frozen Mistral‑7B backbone. By adding discriminative field‑classifier heads over per‑lesion features, NeuroFusion restores accurate diagnoses (meningioma 0.92, metastasis 0.75) and improves prose quality while reducing latency 5–6×. A controlled negative result shows that overriding the decoder with a learned diagnosis pin harms performance, and grammar‑constrained decoding yields high schema‑validity (92.3%).
By Khawaja Murad ul Hassan, Ruqiyya Adil, Adil Qayyum, Rida Hassan, Asad Mansoor Khan, Muhammad Usman Akram, Mehran Ebrahimi
arXiv:2606. 19300v1 Announce Type: cross Abstract: Glioma segmentation in multiparametric MRI is a critical component of treatment planning.
By Xin Ci Wong, Duygu Sarikaya, Kieran Zucker, Marc De Kamps, Nishant Ravikumar
VGG16-MCA UNet is a hybrid neural network that combines an ImageNet‑pretrained VGG16 encoder with a decoder enhanced by a Multi‑Channel Attention module, trained using Focal Tversky loss to address class imbalance. The model was evaluated as a 2‑D, FLAIR‑only whole‑tumor segmenter on BraTS 2020 and LGG datasets, achieving a pixel‑level Dice of 95.10 % on BraTS and 88.32 % on LGG in a 5‑fold cross‑validation setting. Inference time is 66.32 ms per 256×256 slice on a single RTX 2060, only slightly slower than a VGG16‑UNet without attention.
whyItMatters":"The study provides a reproducible 2‑D FLAIR baseline for whole‑tumor segmentation, demonstrating high Dice scores and detailed reporting of training and evaluation protocols."
By Shubham Gajjar, Deep Joshi, Avi Poptani, Vishal Barot
The paper reports the submissions of team nucli to the HECKTOR 2026 challenge, which evaluates machine learning methods for head‑and‑neck cancer imaging. The team used self‑supervised pre‑trained encoders, training a 2D DINOv2 encoder on PET scans and combining a DynUNet with a SwinUNETR for segmentation, achieving a mean Dice of 0.5409. For TN staging, they fused geometric features from segmentation masks with PET‑DINOv2‑MIPs deep features via logistic regression, obtaining balanced accuracies of 0.3962 (T‑stage) and 0.6432 (N‑stage). For recurrence‑free survival prognosis, they fine‑tuned the PET‑DINOv2‑MIPs ViT‑S encoder with an MLP head under a CoxPH loss, achieving a Harrell's C‑index of 0.6577 and a weighted challenge score of 0.5802.
Brain tumor segmentation uses complementary magnetic resonance imaging (MRI) sequences, but some sequences may be unavailable in practice. Their absence changes the information available for segmentin...
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
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.
By Eunseob Choi, Kyeonghun Kim, Hyuk-Jae Lee, Nam-Joon Kim
arXiv:2609.15524v1 Announce Type: new
Abstract: BraTS-GoAT evaluates tumor segmentation across heterogeneous populations. We trained a conventional 3D nnU-Net on 1,351 labeled cases using five-fold c...
By Tristan Kirscher (ICube, Institut Strauss), Vivian Metzger (Institut Strauss), Philippe Meyer (Institut Strauss, ICube), Xavier Coubez (Institut Strauss, ICube)
arXiv:2608. 16377v1 Announce Type: cross Abstract: Instance-level lesion detection has been an increasingly larger focal point in medical image segmentation besides the more standard voxel-level overlap.
By Qinghui Liu, Jon Andr\'e Ottesen, Atle Bj{\o}rnerud, Kyrre Eeg Emblem