Supervoxel Classification Forests for Estimating Pairwise Image Correspondences. - Fahdi Kanavati; Tong Tong 001; Kazunari Misawa; Michitaka Fujiwara; Kensaku Mori; Daniel Rueckert; Ben Glocker (MLMI, 2015)

Joint Supervoxel Classification Forest for Weakly-Supervised Organ Segmentation. - Fahdi Kanavati; Kazunari Misawa; Michitaka Fujiwara; Kensaku Mori; Daniel Rueckert; Ben Glocker (MLMI@MICCAI, 2017)

Efficient extraction of semantic information from medical images in large datasets using random forests. - Fahdi Kanavati (2017)

Automatic L3 slice detection in 3D CT images using fully-convolutional networks. - Fahdi Kanavati; Shah Islam; Eric O. Aboagye; Andrea G. Rockall (CoRR, 2018)

Fully-automated deep learning slice-based muscle estimation from CT images for sarcopenia assessment. - Fahdi Kanavati; Shah Islam; Zohaib Arain; Eric O. Aboagye; Andrea G. Rockall (CoRR, 2020)

Bayesian Sampling Bias Correction: Training with the Right Loss Function. - Loïc Le Folgoc; Vasileios Baltatzis; Amir Alansary; Sujal Desai; Anand Devaraj; Sam Ellis; Octavio E. Martinez Manzanera; Fahdi Kanavati; Arjun Nair; Julia A. Schnabel; Ben Glocker (CoRR, 2020)

Deep learning models for gastric signet ring cell carcinoma classification in whole slide images. - Fahdi Kanavati; Shin Ichihara; Michael Rambeau; Osamu Iizuka; Koji Arihiro; Masayuki Tsuneki (CoRR, 2020)

Partial transfusion: on the expressive influence of trainable batch norm parameters for transfer learning. - Fahdi Kanavati; Masayuki Tsuneki (MIDL, 2021)

A deep learning model for gastric diffuse-type adenocarcinoma classification in whole slide images. - Fahdi Kanavati; Masayuki Tsuneki (CoRR, 2021)

Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data. - Loïc Le Folgoc; Vasileios Baltatzis; Amir Alansary; Sujal Desai; Anand Devaraj; Sam Ellis; Octavio E. Martinez Manzanera; Fahdi Kanavati; Arjun Nair; Julia A. Schnabel; Ben Glocker (CoRR, 2021)

Inference of captions from histopathological patches. - Masayuki Tsuneki; Fahdi Kanavati (MIDL, 2022)

σ-PCA: a unified neural model for linear and nonlinear principal component analysis. - Fahdi Kanavati; Lucy Katsnith; Masayuki Tsuneki (CoRR, 2023)

σ-PCA: a building block for neural learning of identifiable linear transformations. - Fahdi Kanavati; Lucy Katsnith; Masayuki Tsuneki (Trans. Mach. Learn. Res., 2024)