38 citations · 50 across the 6 of their papers we have counts for
7 papers
The Federated Tumor Segmentation and Classification (FeTS++) Challenge
Wenyi Tang, Carla Pitarch-Abaigar, Siddhesh Thakur +5
International challenges have become the standard for developing and benchmarking biomedical image analysis methods using centralized multi-institutional data. Federated learning (…
Multi-FRuGaL: Multimodal Flexible Redundancy-aware Decomposed Gated Learning for Cancer Diagnosis and Prognosis
Sanket Kachole, Siddhesh Thakur, Shubham Innani +4
Modern medicine relies on heterogeneous data sources spanning radiology, pathology, text reports, and structured clinical information. However, real-world patient data are frequent…
Skull stripping with purely synthetic data
Jong Sung Park, Juhyung Ha, Siddhesh Thakur +3
While many skull stripping algorithms have been developed for multi-modal and multi-species cases, there is still a lack of a fundamentally generalizable approach. We present PUMBA…
Biochemical Prostate Cancer Recurrence Prediction: Thinking Fast & Slow
Suhang You, Sanyukta Adap, Siddhesh Thakur +2
Time to biochemical recurrence in prostate cancer is essential for prognostic monitoring of the progression of patients after prostatectomy, which assesses the efficacy of the surg…
BraTS-Path Challenge: Assessing Heterogeneous Histopathologic Brain Tumor Sub-regions
Spyridon Bakas, Siddhesh P. Thakur, Shahriar Faghani +19
Glioblastoma is the most common primary adult brain tumor, with a grim prognosis - median survival of 12-18 months following treatment, and 4 months otherwise. Glioblastoma is wide…
Federated Learning for the Classification of Tumor Infiltrating Lymphocytes
Ujjwal Baid, Sarthak Pati, Tahsin M. Kurc +6
We evaluate the performance of federated learning (FL) in developing deep learning models for analysis of digitized tissue sections. A classification application was considered as…
GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
Sarthak Pati, Siddhesh P. Thakur, İbrahim Ethem Hamamcı +39
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities. However, greater expertise is required to develop DL algorithms,…