PANIC, JOVANA
 Distribuzione geografica
Continente #
EU - Europa 173
NA - Nord America 39
AS - Asia 5
AF - Africa 1
SA - Sud America 1
Totale 219
Nazione #
IT - Italia 107
US - Stati Uniti d'America 35
GB - Regno Unito 34
RU - Federazione Russa 8
IE - Irlanda 6
FR - Francia 5
CA - Canada 4
CZ - Repubblica Ceca 4
HK - Hong Kong 4
ES - Italia 3
UA - Ucraina 2
CN - Cina 1
DE - Germania 1
FI - Finlandia 1
PE - Perù 1
PL - Polonia 1
SE - Svezia 1
UG - Uganda 1
Totale 219
Città #
Southend 30
Turin 29
Torino 18
Milan 8
Dublin 6
Mcallen 5
Ann Arbor 4
Toronto 4
Torrazza 4
Ashburn 3
Houston 3
Milpitas 3
Barcelona 2
Bottanuco 2
Buffalo 2
Central District 2
Mazzè 2
Prato 2
Rome 2
Basking Ridge 1
Beijing 1
Bremen 1
Cambridge 1
Chieti 1
Helsinki 1
Kampala 1
Leawood 1
Lima 1
Madrid 1
Messina 1
Napoli 1
Palo Alto 1
Paris 1
Portici 1
San Giuliano Milanese 1
San Mateo 1
Varese 1
Vinnytsia 1
Washington 1
Wilmington 1
Totale 152
Nome #
Deep learning to segment liver metastases on CT images: Impact on a radiomics method to predict response to chemotherapy, file e384c432-3b38-d4b2-e053-9f05fe0a1d67 74
An innovative radiomics approach to predict response to chemotherapy of liver metastases based on CT images, file e384c432-4b55-d4b2-e053-9f05fe0a1d67 37
A Convolutional Neural Network based system for Colorectal cancer segmentation on MRI images, file e384c432-7873-d4b2-e053-9f05fe0a1d67 30
Comparison of different classifiers to recognize active bone marrow from CT images, file e384c432-6b04-d4b2-e053-9f05fe0a1d67 23
Deep learning model for automatic prostate segmentation on bicentric T2w images with and without endorectal coil, file e384c434-6f74-d4b2-e053-9f05fe0a1d67 9
A fully automatic deep learning algorithm to segment Rectal Cancer on MR images: a multi-center study, file 4c16d833-78d5-4332-a9f9-748aff79156e 6
Comparison of radiomics approaches to predict resistance to 1st line chemotherapy in liver metastatic colorectal cancer, file e384c434-8176-d4b2-e053-9f05fe0a1d67 6
Impact of network parameters on a U-Net based system for rectal cancer segmentation on MR images, file e384c434-9b8e-d4b2-e053-9f05fe0a1d67 6
A Deep Learning model to segment liver metastases on CT images acquired at different time-points during chemotherapy, file 4be04809-9769-4cc5-9fa5-ef8188e4ddcf 5
How to Design, Implement, and Validate MRI-based Artificial Intelligence Systems for Oncological applications, file a663be17-a594-4320-9cef-0e09f6ac642a 5
Comparison of different classifiers to recognize active bone marrow from CT images, file e384c432-6b05-d4b2-e053-9f05fe0a1d67 5
Could normalization improve robustness of abdominal MRI radiomic features?, file 37e1b951-f51e-4cc1-a02a-ad838579b384 3
Deep learning model for automatic prostate segmentation on bicentric T2w images with and without endorectal coil, file e384c434-8519-d4b2-e053-9f05fe0a1d67 3
Normalization strategies in multi-center radiomics abdominal MRI: systematic review and meta-analyses, file 03b8430b-d00a-4147-aa31-01d0d223a237 2
Virtual biopsy in abdominal pathology: where do we stand?, file 1e0e0207-38a6-42b2-a876-c3c3191e991c 2
MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study, file af625ccc-faed-44d2-9a70-a428bf7da917 2
An innovative radiomics approach to predict response to chemotherapy of liver metastases based on CT images, file e384c432-4916-d4b2-e053-9f05fe0a1d67 2
Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice, file 0a4d47c6-52fe-4e9c-8be7-61be1494f65c 1
A Deep Learning model to segment liver metastases on CT images acquired at different time-points during chemotherapy, file 10d6dfb3-6c6d-44e1-aabe-74a05a273a29 1
A fully automatic deep learning algorithm to segment Rectal Cancer on MR images: a multi-center study, file 7c17bced-d9aa-4f29-8bdc-fda5357bd72b 1
A Convolutional Neural Network based system for Colorectal cancer segmentation on MRI images, file e384c432-4b54-d4b2-e053-9f05fe0a1d67 1
Comparison of radiomics approaches to predict resistance to 1st line chemotherapy in liver metastatic colorectal cancer, file e384c434-b01a-d4b2-e053-9f05fe0a1d67 1
Totale 225
Categoria #
all - tutte 532
article - articoli 66
book - libri 0
conference - conferenze 451
curatela - curatele 0
other - altro 0
patent - brevetti 0
selected - selezionate 0
volume - volumi 0
Totale 1.049


Totale Lug Ago Sett Ott Nov Dic Gen Feb Mar Apr Mag Giu
2020/202175 0 0 8 3 10 8 8 6 14 6 4 8
2021/202295 10 9 8 6 6 7 9 15 2 7 10 6
2022/202347 4 2 13 2 4 4 4 0 2 2 9 1
2023/20248 0 0 1 0 0 0 1 0 0 5 1 0
Totale 225