Brain Tumors are abnormal mass of clustered cells penetrating regions of brain. Their timely identification and classification help doctors to provide appropriate treatment. However, Classifi-cation of Brain Tumors is quite intricate because of high-intra class similarity and low-inter class variability. Due to morphological similarity amongst various MRI-Slices of different classes the challenge deepens more. This all leads to hampering generalizability of classification models. To this end, this paper proposes HSADML, a novel framework which enables deep metric learning (DML) using SphereFace Loss. SphereFace loss embeds the features into a hyperspheric-manifold and then imposes margin on the embeddings to enhance differentiability between the classes. With utilization of SphereFace loss based deep metric learning it is ensured that samples from class clustered together while the different ones are pushed apart. Results reflects the promi-nence in the approach, the proposed framework achieved state-of-the-art 98.69% validation accu-racy using k-NN (k=1) and this is significantly higher than normal SoftMax Loss training which though obtains 98.47% validation accuracy but that too with limited inter-class separability and intra-class closeness. Experimental analysis done over various classifiers and loss function set-tings suggests potential in the approach.
Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification
Hamburg University of Technology（汉堡工业大学）；Carl von Ossietzky University Oldenburg（奥尔登堡大学）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；University of Bonn（波恩大学）；Klinikum Braunschweig（不伦瑞克医院）
Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations
University of Las Palmas de Gran Canaria（大加那利岛拉斯帕尔马斯大学）；Imperial College London（伦敦帝国理工学院）；University Hospital Doctor Negrin（内格林大学医院）；University Hospital Southampton（南安普顿大学医院）；University of Cambridge（剑桥大学）；Universidad Politecnica de Madrid（马德里理工大学）
Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing Modalities
College of Computer and Control Engineering, Northeast Forestry University（东北林业大学计算机与控制工程学院）；The Centre for Healthy Brain Ageing (CHeBA), UNSW（健康脑年龄中心（CHeBA），UNSW）；School of Computer Science and Engineering, UNSW（计算机科学与工程学院，UNSW）；School of Software, Tsinghua University（清华大学软件学院）；Centre for Health Informatics, Macquarie University（健康信息学中心，麦考瑞大学）
Intra-operative Brain Tumor Detection with Deep Learning-Optimized Hyperspectral Imaging
Carl Zeiss AG（卡尔蔡司股份公司）；University of Padua（帕多瓦大学）；University of Applied Sciences Western Switzerland (HES-SO Valais)（瑞士西部应用科学大学（瓦莱州高等专业学院））；Carl Zeiss Meditec AG（卡尔蔡司医疗科技股份公司）；IMEC（比利时微电子研究中心）；University Hospitals Leuven（鲁汶大学医院）；KU Leuven（鲁汶大学）
Faculty of Computer Science, AGH University of Krakow（计算机科学系，克拉科夫AGH大学）；Department of Informatics, Universitas Pembangunan Nasional Veteran Yogyakarta（信息系，全国 veterans 大学 Yogya 市）
SLIMBRAIN: Augmented Reality Real-Time Acquisition and Processing System For Hyperspectral Classification Mapping with Depth Information for In-Vivo Surgical Procedures
Research Center on Software Technologies and Multimedia Systems, Universidad Politécnica de Madrid（马德里理工大学软件技术与多媒体系统研究中心）；Department of Neurosurgery, Hospital Universitario 12 de Octubre, Universidad Complutense de Madrid（马德里康普顿斯大学十月十二日大学医院神经外科）
Topology-Driven Fusion of nnU-Net and MedNeXt for Accurate Brain Tumor Segmentation on Sub-Saharan Africa Dataset
Institute of Engineering, Thapathali Campus, Nepal（尼泊尔工程学院，塔帕塔利校区）；Gandaki College of Engineering and Science, Pokhara University, Pokhara, Nepal（加德基工程与科学学院，博克拉大学，博克拉，尼泊尔）；Nepal Engineering College, Changunarayan-4, Bhaktapur, Nepal（尼泊尔工程学院，恰纳拉亚纳-4，巴赫塔普尔，尼泊尔）；Madan Bhandari University of Science and Technology, Chitlang, Nepal（马丹·班达里科学与技术大学，奇塔朗，尼泊尔）；Montreal Neurological Institute, McGill University, Montreal, QC, Canada（蒙特利尔神经研究所，麦吉尔大学，蒙特利尔，魁北克，加拿大）；Department of Biomedical Engineering, McGill University（麦吉尔大学生物医学工程系）；Medical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria（医学人工智能实验室（MAI实验室），拉各斯，尼日利亚）；Department of Physics, Federal University of Technology, Minna, Nigeria（物理系，联邦技术大学，米纳，尼日利亚）；Department of Computer Science, Johns Hopkins University（计算机科学系，约翰霍普金斯大学）；Department of Radiology and Radiological Science, Johns Hopkins School of Medicine（放射学与放射科学系，约翰霍普金斯医学院）；Nepal Applied Mathematics and Informatics Institute for Research (NAAMII), Nepal（尼泊尔应用数学与信息学研究所（NAAMII），尼泊尔）；Institute of Engineering, Purbanchal Campus, Nepal（尼泊尔工程学院，普尔班恰尔校区）
Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer Analysis
The Chinese University of Hong Kong（香港中文大学）；Imsight Medical Technology, Co., Ltd.（推想医疗科技股份有限公司）；Imperial College London（伦敦帝国理工学院）；The Fifth Medical Center of Chinese PLA General Hospital（中国人民解放军总医院第五医学中心）；Beijing Image Diagnostic Center of Rimag（北京一脉阳光影像诊断中心）
The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma
Duke University Medical Center（杜克大学医学中心）；Crestview Radiology（远景放射科）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；University of Pennsylvania（宾夕法尼亚大学）；Missouri University（密苏里大学）；Children's National Hospital（国家儿童医院）；George Washington University（乔治华盛顿大学）；Sage Bionetworks（赛智生物网络公司）；University of California San Francisco（加州大学旧金山分校）；Mayo Clinic（梅奥诊所）；Technical University Munich（慕尼黑工业大学）；Technical University of Munich（慕尼黑工业大学）；Children’s Hospital of Philadelphia（费城儿童医院）；National Cancer Institute, National Institutes of Health（美国国立卫生研究院国家癌症研究所）；Massachusetts General Hospital（马萨诸塞州总医院）；U.S. Food and Drug Administration（美国食品药品监督管理局）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；Technical University of Denmark（丹麦技术大学）；University of Zurich（苏黎世大学）；Thomas Jefferson University（托马斯杰斐逊大学）；Yale University（耶鲁大学）；Booz Allen Hamilton（博思艾伦咨询公司）；Mercy Catholic Medical Center（仁慈天主教医疗中心）；Northwestern University（西北大学）；McGill University（麦吉尔大学）；University of San Diego（圣地亚哥大学）
