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