most citedDeep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

q-bio.TO2026

A deep learning framework for glomeruli segmentation with boundary attention

Behnaz Elhaminia, Catherine King, Jiaqi Lv +5

Accurate detection and segmentation of glomeruli in kidney tissue are essential for diagnostic applications. Traditional deep learning methods primarily rely on semantic segmentati…

eess.IV2025

MitoDetect++: A Domain-Robust Pipeline for Mitosis Detection and Atypical Subtyping

Esha Sadia Nasir, Jiaqi Lv, Mostafa Jahanifar +1

Automated detection and classification of mitotic figures especially distinguishing atypical from normal remain critical challenges in computational pathology. We present MitoDetec…

cs.LG2025

ModalSurv: Investigating opportunities and limitations of multimodal deep survival learning in prostate and bladder cancer

Noorul Wahab, Ethar Alzaid, Jiaqi Lv +3

Accurate survival prediction is essential for personalised cancer treatment. We propose ModalSurv, a multimodal deep survival framework integrating clinical, MRI, histopathology, a…

eess.IV2025

Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images

Jiaqi Lv, Yijie Zhu, Carmen Guadalupe Colin Tenorio +3

Melanoma is an aggressive form of skin cancer with rapid progression and high metastatic potential. Accurate characterisation of tissue morphology in melanoma is crucial for progno…

eess.IV20251 cited

Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images

Jiaqi Lv, Stefan S Antonowicz, Shan E Ahmed Raza

Blood vessels (BVs) play a critical role in the Tumor Micro-Environment (TME), potentially influencing cancer progression and treatment response. However, manually quantifying BVs…