The Lancet

Deep learning for H&E-based meningioma molecular classification and outcome prediction: a retrospective cohort study

To our knowledge, this study is the first to apply deep learning models that can, beyond diagnosis, identify molecular subtypes and predict outcomes in a single brain tumour entity (meningioma) using ...
IEEE

PBH: A Periodic Binary Head with Two-Stage Training for Efficient Text Classification

Abstract: Quantized neural networks significantly reduce storage requirements and computational complexity by lowering the numerical precision of weights and activations. Among these, binary neural ...
Scientific Research Publishing

Chicco, D. and Jurman, G. (2020) The Advantages of the Matthews Correlation Coefficient (MCC) over F1 Score and Accuracy in Binary Classification Evaluation. BMC Genomics, 21 ...

ABSTRACT: Automatic detection of cognitive distortions from short written text could support large-scale mental-health screening and digital cognitive-behavioural therapy (CBT). Many recent approaches ...
marktechpost

Focal Loss vs Binary Cross-Entropy: A Practical Guide for Imbalanced Classification

Binary cross-entropy (BCE) is the default loss function for binary classification—but it breaks down badly on imbalanced datasets. The reason is subtle but important: BCE weighs mistakes from both ...
Scientific Research Publishing

Li, J. (2024) Area under the ROC Curve Has the Most Consistent Evaluation for Binary Classification. PLOS ONE, 19, e0316019.

ABSTRACT: The advent of the internet, as we all know, has brought about a significant change in human interaction and business operations around the world; yet, this evolution has also been marked by ...
IEEE

Shape Penalized Decision Forests for Imbalanced Data Classification

Abstract: Class imbalance poses a critical challenge in binary classification problems, particularly when rare but significant events are underrepresented in the training set. While traditional ...