IP Library › Granted Patent US 12,504,363
Granted Patent B2
US 12,504,363 · App. 17/820,496 · Granted Dec 23, 2025

Three-dimensional contoured scanning photoacoustic imaging and virtual staining

Inventors: Lihong Wang (Arcadia, CA); Rui Cao (Pasadena, CA)
Assignee: CALIFORNIA INSTITUTE OF TECHNOLOGY
G01N21/1702G06V10/82G06V20/69H04N13/15H04N13/254H04N13/271G01N2021/1706G01N2021/1772G01N2201/067G01N2201/106G01N2201/127
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,504,363
App. No.
17/820,496
Granted
Dec 23, 2025
Kind
B2
Abstract

Methods, devices, apparatus, and systems for three-dimensional (3D) contoured scanning photoacoustic imaging and/or deep learning virtual staining.

Claims (45)

1 . A method for virtually staining a photoacoustic histology image, the method comprising:

generating a virtually stained histology image using a first generative adversarial network trained to predict, from the photoacoustic histology image, the virtually stained histology image having a characteristic of hematoxylin and eosin staining;

wherein the first generative adversarial network has been trained by:

inputting a training photoacoustic histology image to a first generator configured to generate a first predicted image having a characteristic of hematoxylin and eosin staining;

inputting the first predicted image to a second generator of a second generative adversarial network, the second generator configured to generate a second predicted image having a characteristic of photoacoustic imaging; and

based on an error associated with the training photoacoustic histology image and the second predicted image, updating one or both of the first generator or the second generator.

2 . The method of claim 1 , wherein the generating of the virtually stained histology image having the characteristic of hematoxylin and eosin staining is performed in 5 seconds or less.

3 . The method of claim 1 , further comprising recovering the photoacoustic histology image using a second generative adversarial network trained to predict, from the predicted virtually stained histology image, a histology image having a characteristic of the photoacoustic histology image.

4 . The method of claim 3 , wherein the first generative adversarial network and the second generative adversarial network are trained to operate as a cycle-consistent generative adversarial network configured such that the photoacoustic histology image input to the first generative adversarial network matches the predicted histology image having the characteristic of the photoacoustic histology image predicted by the second generative adversarial network.

5 . The method of claim 3 , wherein the second generative adversarial network is further trained to generate, from a hematoxylin and eosin staining image, a predicted photoacoustic histology image having a characteristic of photoacoustic histology.

6 . The method of claim 4 , wherein the first generative adversarial network is further trained to recover the hematoxylin and eosin staining image by predicting, from the predicted photoacoustic histology image, a second stained histology image having a characteristic of hematoxylin and eosin staining.

7 . The method of claim 1 , wherein:

the first generative adversarial network comprises the first generator and a corresponding first adversarial discriminator;

using the first generative adversarial network to predict the virtually stained histology image comprises using the first generator to predict the virtually stained histology image; and

the first adversarial discriminator comprises a classifier trained to output a determination that the predicted virtually stained histology image is real or fake.

8 . The method of claim 7 , wherein:

the first generator comprises one or more first layers of the first generative adversarial network, the one or more first layers including one or more first convolutional layers, one or more first downsampling layers, one or more first upsampling layers, or a combination thereof;

the first adversarial discriminator comprises one or more second layers of the first generative adversarial network, the one or more second layers including one or more convolutional layers, one or more downsampling layers, or a combination thereof; and

the output of the determination by the first adversarial discriminator is based on the one or more second layers.

9 . A method for generating virtual histology images, the method comprising;

using a first generator of a first generative adversarial network to generate a virtually stained histology image having a characteristic of hematoxylin and eosin staining, the first generator trained to generate the virtually stained histology image based on an input of a photoacoustic histology image;

wherein the first generator is trained by:

inputting a training photoacoustic histology image to the first generator, wherein the training photoacoustic histology image comprises a real image or a fake image;

predicting an output image having the characteristic of hematoxylin and eosin staining;

obtaining an initial metric value ranging from 1 to 0, the initial metric value representative of a likelihood that the predicted output image is real or fake generated by a first adversarial discriminator corresponding to the first generator; and

updating the first generator such that a subsequent metric value generated by the first adversarial discriminator is closer to 0.5 than the initial metric value.

10 . The method of claim 9 , further comprising recovering the photoacoustic histology image by using a second generator of a second generative adversarial network, the second generator trained to predict, based on an input of the virtually stained histology image, a histology image having a characteristic of the photoacoustic histology image.

11 . The method of claim 10 , wherein the first generative adversarial network and the second generative adversarial network are trained to operate as a cycle-consistent generative adversarial network configured such that the photoacoustic histology image input to the first generator matches the predicted histology image having the characteristic of the photoacoustic histology image predicted by the second generator.

12 . The method of claim 10 , wherein the second generator is trained to generate, from a hematoxylin and eosin stained image, a predicted photoacoustic histology image having a characteristic of photoacoustic histology.

13 . The method of claim 12 , wherein the first generator is further trained to recover the hematoxylin and eosin stained image by predicting, from the predicted photoacoustic histology image, a second virtually stained histology image having a characteristic of hematoxylin and eosin staining.

14 . A method for generating virtual histology images, the method comprising;

using a first generator of a first generative adversarial network to generate a virtually stained histology image having a characteristic of hematoxylin and eosin staining, the first generator trained to predict the virtually stained histology image based on an input of a photoacoustic histology image;

wherein:

the first generator comprises one or more first layers of the first generative adversarial network, the one or more first layers including one or more first convolutional layers, one or more first downsampling layers, one or more first upsampling layers, or a combination thereof;

a first adversarial discriminator corresponding to the first generator comprises one or more second layers of the first generative adversarial network, the one or more second layers including one or more convolutional layers, one or more downsampling layers, or a combination thereof; and

the first adversarial discriminator is trained to output a determination that the predicted virtually stained histology image is real or fake based on the one or more second layers.

15 . A method for virtually staining a photoacoustic histology image, the method comprising:

generating a virtually stained histology image using a first generative adversarial network trained to predict, from the photoacoustic histology image, the virtually stained histology image having a characteristic of hematoxylin and eosin staining;

wherein:

the first generative adversarial network comprises a first generator and a corresponding first adversarial discriminator;

using the first generative adversarial network to predict the virtually stained histology image comprises using the first generator to predict the virtually stained histology image;

the first adversarial discriminator comprises a classifier trained to output a determination that the predicted virtually stained histology image is real or fake;

the first generator comprises one or more first layers of the first generative adversarial network, the one or more first layers including one or more first convolutional layers, one or more first downsampling layers, one or more first upsampling layers, or a combination thereof;

the first adversarial discriminator comprises one or more second layers of the first generative adversarial network, the one or more second layers including one or more convolutional layers, one or more downsampling layers, or a combination thereof; and

the output of the determination by the first adversarial discriminator is based on the one or more second layers.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 16, 2024
From: WANG, LIHONG; CAO, RUI
To: CALIFORNIA INSTITUTE OF TECHNOLOGY
Reel/Frame 068915/0443 →
Continuity (2)
Provisional Application 63234080 · Aug 17, 2021
Related Publication 20230055979A1 · Feb 23, 2023
References Cited (400)
US 4029756A · Gaafar · 1977 [cited by applicant]
US 4127318A · Determann et al. · 1978 [cited by applicant]
US 4255971A · Rosencwaig · 1981 [cited by applicant]
US 4267732A · Quate · 1981 [cited by applicant]
US 4284324A · Huignard et al. · 1981 [cited by applicant]
US 4375818A · Suwaki et al. · 1983 [cited by applicant]
US 4385634A · Bowen · 1983 [cited by applicant]
US 4430897A · Quate · 1984 [cited by applicant]
US 4430987A · Heller · 1984 [cited by applicant]
US 4462255A · Guess et al. · 1984 [cited by applicant]
US 4468136A · Murphy et al. · 1984 [cited by applicant]
US 4489727A · Matsuo et al. · 1984 [cited by applicant]
US 4546771A · Eggleton et al. · 1985 [cited by applicant]
US 4596254A · Adrian et al. · 1986 [cited by applicant]
US 4687304A · Piller et al. · 1987 [cited by applicant]
US 4740081A · Martens et al. · 1988 [cited by applicant]
US 4802461A · Cho · 1989 [cited by applicant]
US 4802487A · Martin et al. · 1989 [cited by applicant]
US 4809703A · Ishikawa et al. · 1989 [cited by applicant]
US 4850363A · Yanagawa · 1989 [cited by applicant]
US 4860758A · Yanagawa et al. · 1989 [cited by applicant]
US 4869256A · Kanno et al. · 1989 [cited by applicant]
US 4872758A · Miyazaki et al. · 1989 [cited by applicant]
US 4921333A · Brody et al. · 1990 [cited by applicant]
US 4929951A · Small · 1990 [cited by applicant]
US 4995396A · Inaba et al. · 1991 [cited by applicant]
US 5070455A · Singer et al. · 1991 [cited by applicant]
US 5083549A · Cho et al. · 1992 [cited by applicant]
US 5107844A · Kami et al. · 1992 [cited by applicant]
US 5115814A · Griffith et al. · 1992 [cited by applicant]
US 5125410A · Misono et al. · 1992 [cited by applicant]
US 5140463A · Yoo et al. · 1992 [cited by applicant]
US 5170793A · Takano et al. · 1992 [cited by applicant]
US 5194723A · Cates et al. · 1993 [cited by applicant]
US 5207672A · Roth et al. · 1993 [cited by applicant]
US 5227912A · Ho et al. · 1993 [cited by applicant]
US 5305759A · Kaneko et al. · 1994 [cited by applicant]
US 5321501A · Swanson et al. · 1994 [cited by applicant]
US 5329817A · Garlick et al. · 1994 [cited by applicant]
US 5331466A · Van Saarloos · 1994 [cited by applicant]
US 5345938A · Nishiki et al. · 1994 [cited by applicant]
US 5373845A · Gardineer et al. · 1994 [cited by applicant]
US 5414623A · Lu et al. · 1995 [cited by applicant]
US 5445155A · Sieben · 1995 [cited by applicant]
US 5465722A · Fort et al. · 1995 [cited by applicant]
US 5546187A · Pepper et al. · 1996 [cited by applicant]
US 5546947A · Yagami et al. · 1996 [cited by applicant]
US 5546948A · Hamm et al. · 1996 [cited by applicant]
US 5606975A · Liang et al. · 1997 [cited by applicant]
US 5615675A · O'Donnell et al. · 1997 [cited by applicant]
US 5635784A · Seale · 1997 [cited by applicant]
US 5651366A · Liang et al. · 1997 [cited by applicant]
US 5713356A · Kruger · 1998 [cited by applicant]
US 5718231A · Dewhurst et al. · 1998 [cited by applicant]
US 5781294A · Nakata et al. · 1998 [cited by applicant]
US 5836872A · Kenet et al. · 1998 [cited by applicant]
US 5840023A · Oraevsky et al. · 1998 [cited by applicant]
US 5860934A · Sarvazyan · 1999 [cited by applicant]
US 5913234A · Julliard et al. · 1999 [cited by applicant]
US 5971998A · Russell et al. · 1999 [cited by applicant]
US 5977538A · Unger et al. · 1999 [cited by applicant]
US 5991697A · Nelson et al. · 1999 [cited by applicant]
US 6055097A · Lanni et al. · 2000 [cited by applicant]
US 6102857A · Kruger · 2000 [cited by applicant]
US 6104942A · Kruger · 2000 [cited by applicant]
US 6108576A · Alfano et al. · 2000 [cited by applicant]
US 6111645A · Tearney et al. · 2000 [cited by applicant]
US 6134003A · Tearney et al. · 2000 [cited by applicant]
US 6216025B1 · Kruger · 2001 [cited by applicant]
US 6233055B1 · Mandella et al. · 2001 [cited by applicant]
US 6282011B1 · Tearney et al. · 2001 [cited by applicant]
US 6292682B1 · Kruger · 2001 [cited by applicant]
US 6309352B1 · Oraevsky et al. · 2001 [cited by applicant]
US 6341036B1 · Tearney et al. · 2002 [cited by applicant]
US 6379325B1 · Benett et al. · 2002 [cited by applicant]
US 6405069B1 · Oraevsky et al. · 2002 [cited by applicant]
US 6409671B1 · Eriksen et al. · 2002 [cited by applicant]
US 6413228B1 · Hung et al. · 2002 [cited by applicant]
US 6421164B2 · Tearney et al. · 2002 [cited by applicant]
US 6432067B1 · Martin et al. · 2002 [cited by applicant]
US 6466806B1 · Geva et al. · 2002 [cited by applicant]
US 6485413B1 · Boppart et al. · 2002 [cited by applicant]
US 6490470B1 · Kruger · 2002 [cited by applicant]
US 6498942B1 · Esenaliev et al. · 2002 [cited by applicant]
US 6498945B1 · Alfheim et al. · 2002 [cited by applicant]
US 6501551B1 · Tearney et al. · 2002 [cited by applicant]
US 6545264B1 · Stern · 2003 [cited by applicant]
US 6564087B1 · Pitris et al. · 2003 [cited by applicant]
US 6567688B1 · Wang · 2003 [cited by applicant]
US 6590830B1 · Garlick et al. · 2003 [cited by applicant]
US 6626834B2 · Dunne et al. · 2003 [cited by applicant]
US 6628404B1 · Kelley et al. · 2003 [cited by applicant]
US 6633774B2 · Kruger · 2003 [cited by applicant]
US 6654630B2 · Zuluaga et al. · 2003 [cited by applicant]
US 6658279B2 · Swanson et al. · 2003 [cited by applicant]
US 6694173B1 · Bende et al. · 2004 [cited by applicant]
US 6701181B2 · Tang et al. · 2004 [cited by applicant]
US 6751490B2 · Esenaliev et al. · 2004 [cited by applicant]
US 6764450B2 · Yock · 2004 [cited by applicant]
US 6831781B2 · Tearney et al. · 2004 [cited by applicant]
US 6833540B2 · Mackenzie et al. · 2004 [cited by applicant]
US 6839496B1 · Mills et al. · 2005 [cited by applicant]
US 6846288B2 · Nagar et al. · 2005 [cited by applicant]
US 6853446B1 · Almogy et al. · 2005 [cited by applicant]
US 6877894B2 · Vona et al. · 2005 [cited by applicant]
US 6937886B2 · Zavislan · 2005 [cited by applicant]
US 6956650B2 · Boas et al. · 2005 [cited by applicant]
US 7072045B2 · Chen et al. · 2006 [cited by applicant]
US 7198778B2 · Achilefu et al. · 2007 [cited by applicant]
US 7231243B2 · Tearney et al. · 2007 [cited by applicant]
US 7245789B2 · Bates et al. · 2007 [cited by applicant]
US 7266407B2 · Li et al. · 2007 [cited by applicant]
US 7322972B2 · Viator et al. · 2008 [cited by applicant]
US 7357029B2 · Falk · 2008 [cited by applicant]
US 7382949B2 · Bouma et al. · 2008 [cited by applicant]
US 7541602B2 · Metzger et al. · 2009 [cited by applicant]
US 7610080B1 · Winchester, Jr. et al. · 2009 [cited by applicant]
US 7917312B2 · Wang et al. · 2011 [cited by applicant]
US 8016419B2 · Zhang et al. · 2011 [cited by applicant]
US 8025406B2 · Zhang et al. · 2011 [cited by applicant]
US 8143605B2 · Metzger et al. · 2012 [cited by applicant]
US 8397573B2 · Kobayashi · 2013 [cited by applicant]
US 8416421B2 · Wang et al. · 2013 [cited by applicant]
US 8454512B2 · Wang et al. · 2013 [cited by applicant]
US 8891088B2 · Goldschmidt et al. · 2014 [cited by applicant]
US 8997572B2 · Wang et al. · 2015 [cited by applicant]
US 9086365B2 · Wang et al. · 2015 [cited by applicant]
US 9096365B2 · Kim · 2015 [cited by applicant]
US 9182387B2 · Goldkorn et al. · 2015 [cited by applicant]
US 9220415B2 · Mandelis et al. · 2015 [cited by applicant]
US 9226666B2 · Wang et al. · 2016 [cited by applicant]
US 9234841B2 · Wang et al. · 2016 [cited by applicant]
US 9335605B2 · Wang et al. · 2016 [cited by applicant]
US 9426455B2 · Horstmeyer et al. · 2016 [cited by applicant]
US 9528966B2 · Wang et al. · 2016 [cited by applicant]
US 9554738B1 · Gulati et al. · 2017 [cited by applicant]
US 9618445B2 · Sun et al. · 2017 [cited by applicant]
US 9739783B1 · Kumar et al. · 2017 [cited by applicant]
US 9864184B2 · Ou et al. · 2018 [cited by applicant]
US 10285595B2 · Zalev et al. · 2019 [cited by applicant]
US 10359400B2 · Wang et al. · 2019 [cited by applicant]
US 10433733B2 · Wang et al. · 2019 [cited by applicant]
US 10448850B2 · Wang et al. · 2019 [cited by applicant]
US 10572996B2 · Eurèn · 2020 [cited by applicant]
US 10665001B2 · Horstmeyer et al. · 2020 [cited by applicant]
US 10666928B2 · Liu · 2020 [cited by examiner]
US 10679763B2 · Zheng et al. · 2020 [cited by applicant]
US 10740896B2 · Georgescu et al. · 2020 [cited by applicant]
US 10992922B2 · Liu · 2021 [cited by examiner]
US 11020006B2 · Wang et al. · 2021 [cited by applicant]
US 11029287B2 · Wang et al. · 2021 [cited by applicant]
US 11135375B2 · Brady et al. · 2021 [cited by applicant]
US 11137375B2 · Wang et al. · 2021 [cited by applicant]
US 11369280B2 · Wang et al. · 2022 [cited by applicant]
US 11530979B2 · Wang et al. · 2022 [cited by applicant]
US 11592652B2 · Wang et al. · 2023 [cited by applicant]
US 11672426B2 · Wang et al. · 2023 [cited by applicant]
US 11893739B2 · Ozcan et al. · 2024 [cited by applicant]
US 11915360B2 · Ozcan et al. · 2024 [cited by applicant]
US 11986269B2 · Wang et al. · 2024 [cited by applicant]
US 12050201B2 · Wang et al. · 2024 [cited by applicant]
US 12166953B2 · Liu · 2024 [cited by examiner]
US 12182940B2 · Li · 2024 [cited by examiner]
US 20010052979A1 · Treado et al. · 2001 [cited by applicant]
US 20020093637A1 · Yuan et al. · 2002 [cited by applicant]
US 20020173780A1 · Altshuler et al. · 2002 [cited by applicant]
US 20020176092A1 · Deck · 2002 [cited by applicant]
US 20030097066A1 · Shelby et al. · 2003 [cited by applicant]
US 20030160957A1 · Oldham et al. · 2003 [cited by applicant]
US 20030160967A1 · Houston et al. · 2003 [cited by applicant]
US 20040030255A1 · Alfano et al. · 2004 [cited by applicant]
US 20040039379A1 · Viator et al. · 2004 [cited by applicant]
US 20040082070A1 · Jones et al. · 2004 [cited by applicant]
US 20040111023A1 · Edic · 2004 [cited by applicant]
US 20040254474A1 · Seibel et al. · 2004 [cited by applicant]
US 20050015002A1 · Dixon et al. · 2005 [cited by applicant]
US 20050028482A1 · Cable et al. · 2005 [cited by applicant]
US 20050085725A1 · Nagar et al. · 2005 [cited by applicant]
US 20050143664A1 · Chen et al. · 2005 [cited by applicant]
US 20050154313A1 · Desilets et al. · 2005 [cited by applicant]
US 20050168749A1 · Ye et al. · 2005 [cited by applicant]
US 20050217381A1 · Falk · 2005 [cited by applicant]
US 20050234315A1 · Mayevsky et al. · 2005 [cited by applicant]
US 20050277824A1 · Aubry et al. · 2005 [cited by applicant]
US 20060055936A1 · Yun et al. · 2006 [cited by applicant]
US 20060058614A1 · Tsujita · 2006 [cited by applicant]
US 20060078196A1 · Sumanaweera et al. · 2006 [cited by applicant]
US 20060122516A1 · Schmidt et al. · 2006 [cited by applicant]
US 20060181791A1 · Van Beek et al. · 2006 [cited by applicant]
US 20060184042A1 · Wang et al. · 2006 [cited by applicant]
US 20060235299A1 · Martinelli · 2006 [cited by applicant]
US 20060247510A1 · Wiemker et al. · 2006 [cited by applicant]
US 20060264717A1 · Pesach et al. · 2006 [cited by applicant]
US 20070075063A1 · Wilbanks et al. · 2007 [cited by applicant]
US 20070088206A1 · Peyman et al. · 2007 [cited by applicant]
US 20070093702A1 · Yu et al. · 2007 [cited by applicant]
US 20070213590A1 · Squicciarini · 2007 [cited by applicant]
US 20070213618A1 · Li et al. · 2007 [cited by applicant]
US 20070213693A1 · Plunkett · 2007 [cited by applicant]
US 20070282200A1 · Johnson et al. · 2007 [cited by applicant]
US 20070299341A1 · Wang et al. · 2007 [cited by applicant]
US 20080029711A1 · Viellerobe et al. · 2008 [cited by applicant]
US 20080037367A1 · Gross et al. · 2008 [cited by applicant]
US 20080088838A1 · Raicu et al. · 2008 [cited by applicant]
US 20080123083A1 · Wang et al. · 2008 [cited by applicant]
US 20080173093A1 · Wang · 2008 [cited by examiner]
US 20080230717A1 · Ashkenazi et al. · 2008 [cited by applicant]
US 20090051900A1 · Moon et al. · 2009 [cited by applicant]
US 20090054763A1 · Wang et al. · 2009 [cited by applicant]
US 20090088631A1 · Dietz et al. · 2009 [cited by applicant]
US 20090112096A1 · Tamura · 2009 [cited by applicant]
US 20090116518A1 · Patel et al. · 2009 [cited by applicant]
US 20090138215A1 · Wang et al. · 2009 [cited by applicant]
US 20090185191A1 · Boppart et al. · 2009 [cited by applicant]
US 20090227997A1 · Wang et al. · 2009 [cited by applicant]
US 20100053618A1 · Nakajima et al. · 2010 [cited by applicant]
US 20100079768A1 · Wang et al. · 2010 [cited by applicant]
US 20100134793A1 · Krishnamachari et al. · 2010 [cited by applicant]
US 20100151188A1 · Ishizuka · 2010 [cited by applicant]
US 20100245766A1 · Zhang et al. · 2010 [cited by applicant]
US 20100245769A1 · Zhang et al. · 2010 [cited by applicant]
US 20100245770A1 · Zhang et al. · 2010 [cited by applicant]
US 20100249562A1 · Zhang et al. · 2010 [cited by applicant]
US 20100268042A1 · Wang et al. · 2010 [cited by applicant]
US 20100285518A1 · Viator et al. · 2010 [cited by applicant]
US 20100309466A1 · Lucassen et al. · 2010 [cited by applicant]
US 20100322497A1 · Dempsey et al. · 2010 [cited by applicant]
US 20110071402A1 · Masumura · 2011 [cited by applicant]
US 20110077526A1 · Zwirn · 2011 [cited by applicant]
US 20110122416A1 · Yang et al. · 2011 [cited by applicant]
US 20110201914A1 · Wang et al. · 2011 [cited by applicant]
US 20110251515A1 · Leuthardt et al. · 2011 [cited by applicant]
US 20110275890A1 · Wang et al. · 2011 [cited by applicant]
US 20110282181A1 · Wang · 2011 [cited by examiner]
US 20110282192A1 · Axelrod et al. · 2011 [cited by applicant]
US 20120065490A1 · Zharov et al. · 2012 [cited by applicant]
US 20120070817A1 · Wang et al. · 2012 [cited by applicant]
US 20120074294A1 · Streuber et al. · 2012 [cited by applicant]
US 20120118052A1 · O'Donnell et al. · 2012 [cited by applicant]
US 20120204648A1 · Wang et al. · 2012 [cited by applicant]
US 20120275262A1 · Song et al. · 2012 [cited by applicant]
US 20120307250A1 · Wang et al. · 2012 [cited by applicant]
US 20130151188A1 · Rokni et al. · 2013 [cited by applicant]
US 20130199299A1 · Wang et al. · 2013 [cited by applicant]
US 20130218002A1 · Kiraly · 2013 [cited by applicant]
US 20130245406A1 · Wang et al. · 2013 [cited by applicant]
US 20130296684A1 · Miller · 2013 [cited by applicant]
US 20140009808A1 · Wang et al. · 2014 [cited by applicant]
US 20140029829A1 · Jiang et al. · 2014 [cited by applicant]
US 20140118529A1 · Zheng et al. · 2014 [cited by applicant]
US 20140142404A1 · Wang · 2014 [cited by examiner]
US 20140356897A1 · Wang · 2014 [cited by examiner]
US 20150005613A1 · Kim et al. · 2015 [cited by applicant]
US 20150036038A1 · Horstmeyer et al. · 2015 [cited by applicant]
US 20150105672A1 · Ishikawa et al. · 2015 [cited by applicant]
US 20150160450A1 · Ou et al. · 2015 [cited by applicant]
US 20150178959A1 · Huang · 2015 [cited by examiner]
US 20150185187A1 · Wang et al. · 2015 [cited by applicant]
US 20150245771A1 · Wang · 2015 [cited by examiner]
US 20150272444A1 · Maslov et al. · 2015 [cited by applicant]
US 20150272446A1 · Wang et al. · 2015 [cited by applicant]
US 20150297176A1 · Rincker et al. · 2015 [cited by applicant]
US 20150316510A1 · Fukushima et al. · 2015 [cited by applicant]
US 20160081558A1 · Wang et al. · 2016 [cited by applicant]
US 20160235305A1 · Wang et al. · 2016 [cited by applicant]
US 20160242651A1 · Wang et al. · 2016 [cited by applicant]
US 20160249812A1 · Wang et al. · 2016 [cited by applicant]
US 20160262628A1 · Wang et al. · 2016 [cited by applicant]
US 20160305914A1 · Wang et al. · 2016 [cited by applicant]
US 20160310083A1 · Wang et al. · 2016 [cited by applicant]
US 20160345886A1 · Wang et al. · 2016 [cited by applicant]
US 20160361042A1 · Razansky et al. · 2016 [cited by applicant]
US 20170065182A1 · Wang et al. · 2017 [cited by applicant]
US 20170105636A1 · Wang et al. · 2017 [cited by applicant]
US 20170367586A9 · Wang et al. · 2017 [cited by applicant]
US 20170372471A1 · Eurèn · 2017 [cited by examiner]
US 20180020920A1 · Ermilov et al. · 2018 [cited by applicant]
US 20180088041A1 · Zhang et al. · 2018 [cited by applicant]
US 20180132728A1 · Wang et al. · 2018 [cited by applicant]
US 20180177407A1 · Hashimoto et al. · 2018 [cited by applicant]
US 20180232883A1 · Sethi et al. · 2018 [cited by applicant]
US 20190008444A1 · Wang et al. · 2019 [cited by applicant]
US 20190008484A1 · Irisawa et al. · 2019 [cited by applicant]
US 20190125583A1 · Wang et al. · 2019 [cited by applicant]
US 20190206056A1 · Georgescu et al. · 2019 [cited by applicant]
US 20190227038A1 · Wang et al. · 2019 [cited by applicant]
US 20190244347A1 · Buckler et al. · 2019 [cited by applicant]
US 20190298304A1 · Igarashi et al. · 2019 [cited by applicant]
US 20190307334A1 · Wang et al. · 2019 [cited by applicant]
US 20190343758A1 · Wang et al. · 2019 [cited by applicant]
US 20190365355A1 · Eldar et al. · 2019 [cited by applicant]
US 20200056986A1 · Wang et al. · 2020 [cited by applicant]
US 20200073103A1 · Wang et al. · 2020 [cited by applicant]
US 20200124691A1 · Douglas et al. · 2020 [cited by applicant]
US 20200268253A1 · Wang et al. · 2020 [cited by applicant]
US 20200275846A1 · Wang et al. · 2020 [cited by applicant]
US 20200294231A1 · Tosun et al. · 2020 [cited by applicant]
US 20200371335A1 · Amthor et al. · 2020 [cited by applicant]
US 20200397523A1 · Gao et al. · 2020 [cited by applicant]
US 20210010976A1 · Wang et al. · 2021 [cited by applicant]
US 20210018742A1 · Stumpe · 2021 [cited by applicant]
US 20210022702A1 · Yoshikawa · 2021 [cited by applicant]
US 20210132005A1 · Wang et al. · 2021 [cited by applicant]
US 20210145399A1 · Xie et al. · 2021 [cited by applicant]
US 20210164883A1 · Imakubo et al. · 2021 [cited by applicant]
US 20210164886A1 · Shirai et al. · 2021 [cited by applicant]
US 20210321874A1 · Wang et al. · 2021 [cited by applicant]
US 20210333241A1 · Wang et al. · 2021 [cited by applicant]
US 20210349075A1 · Bronevetsky et al. · 2021 [cited by applicant]
US 20220028116A1 · Sieckmann et al. · 2022 [cited by applicant]
US 20220058776A1 · Ozcan et al. · 2022 [cited by applicant]
US 20220122313A1 · Ozcan et al. · 2022 [cited by applicant]
US 20220237783A1 · Wong · 2022 [cited by examiner]
US 20220351347A1 · Yang et al. · 2022 [cited by applicant]
US 20230030424A1 · Ozcan et al. · 2023 [cited by applicant]
US 20230085827A1 · Ozcan et al. · 2023 [cited by applicant]
US 20230098031A1 · Weissleder et al. · 2023 [cited by applicant]
US 20230404407A1 · Garrett et al. · 2023 [cited by applicant]
US 20230404520A1 · Zhang et al. · 2023 [cited by applicant]
US 20240020955A1 · Frick · 2024 [cited by examiner]
US 20240065555A1 · Hu · 2024 [cited by applicant]
US 20240241239A1 · Wang et al. · 2024 [cited by applicant]
US 20240341603A1 · Zhang et al. · 2024 [cited by applicant]
US 20240354942A1 · Yang et al. · 2024 [cited by applicant]
US 20240386629A1 · Hu · 2024 [cited by applicant]
CA 3210010A1 · 2022 [cited by applicant]
CN 1883379A · 2006 [cited by applicant]
CN 106338473A · 2017 [cited by applicant]
EP 0012262A1 · 1980 [cited by applicant]
EP 0919180A1 · 1999 [cited by applicant]
EP 1227525A2 · 2002 [cited by applicant]
EP 1493380A1 · 2005 [cited by applicant]
EP 2749208A1 · 2014 [cited by applicant]
EP 3521808A1 · 2019 [cited by applicant]
JP H05126725A · 1993 [cited by applicant]
JP 2000292416A · 2000 [cited by applicant]
JP 4060615B2 · 2008 [cited by applicant]
JP 2009068977A · 2009 [cited by applicant]
JP 2010017426A · 2010 [cited by applicant]
JP 2010040161A · 2010 [cited by applicant]
JP 2012143384A · 2012 [cited by applicant]
JP 2013244122A · 2013 [cited by applicant]
JP 2014124242A · 2014 [cited by applicant]
JP 2014224806A · 2014 [cited by applicant]
JP 2016101260A · 2016 [cited by applicant]
JP 6086718B2 · 2017 [cited by applicant]
JP 6390516B2 · 2018 [cited by applicant]
KR 100946550B1 · 2010 [cited by applicant]
KR 20160091059A · 2016 [cited by applicant]
KR 20170006470A · 2017 [cited by applicant]
WO WO9633656A1 · 1996 [cited by applicant]
WO WO2006111929A1 · 2006 [cited by applicant]
WO WO2007088709A1 · 2007 [cited by applicant]
WO WO2007148239A2 · 2007 [cited by applicant]
WO WO2008062354A1 · 2008 [cited by applicant]
WO WO2008100386A2 · 2008 [cited by applicant]
WO WO2009055705A2 · 2009 [cited by applicant]
WO WO2009141467A1 · 2009 [cited by applicant]
WO WO2009154298A1 · 2009 [cited by applicant]
WO WO2010048258A1 · 2010 [cited by applicant]
WO WO2010080991A2 · 2010 [cited by applicant]
WO WO2011060101A2 · 2011 [cited by applicant]
WO WO2011091360A2 · 2011 [cited by applicant]
WO WO2011127428A2 · 2011 [cited by applicant]
WO WO2012035472A1 · 2012 [cited by applicant]
WO WO2012133295A1 · 2012 [cited by applicant]
WO WO2013086293A1 · 2013 [cited by applicant]
WO WO2015069827A2 · 2015 [cited by applicant]
WO WO2015118881A1 · 2015 [cited by applicant]
WO WO2016081321A2 · 2016 [cited by applicant]
WO WO2018102446A2 · 2018 [cited by applicant]
WO WO2018102467A1 · 2018 [cited by applicant]
WO WO2018116963A1 · 2018 [cited by applicant]
WO WO2018209046A1 · 2018 [cited by applicant]
WO WO2021067754A1 · 2021 [cited by applicant]
An, N., et al., Risk Factors for Brain Metastases in Patients With Non-small-cell Lung Cancer, Cancer medicine, 2018, vol. 7(12), pp. 6357-6364. [cited by applicant]
Bai, B., et al., “Deep Learning-enabled Virtual Histological Staining of Biological Samples,” Light, science & applications, 2023, vol. 12(1), pp. 1-20. [cited by applicant]
Beck A., et al., “A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems” (2009) Siam J. Imaging Sciences, vol. 2, No. 1, pp. 183-202. [cited by applicant]
Binnewies, M., et al., Understanding the Tumor Immune Microenvironment (Time) for Effective Therapy, Nature medicine, 2018, vol. 24(5), pp. 541-550. [cited by applicant]
Bunn, P., “Early-Stage Non-Small-Cell Lung Cancer: Current Perspectives in Combined-Modality Therapy,” Journal of the National Comprehensive Cancer Network, 2004, vol. 6(2), pp. 85-98. [cited by applicant]
Bychkov, D., et al., Deep learning based tissue analysis predicts outcome in colorectal cancer, Scientific reports, 2018, vol. 8(1), 1-11. [cited by applicant]
Campanella, G., et al., Clinical-grade Computational Pathology Using Weakly Supervised Deep Learning on Whole Slide Images, Nature medicine, 2019, vol. 25(8), pp. 1301-1309. [cited by applicant]
Carolan, H., et al., “Does the Incidence and Outcome of Brain Metastases in Locally Advanced Non-small Cell Lung Cancer Justify Prophylactic Cranial Irradiation or Early Detection?,” Lung cancer, 2005, vol. 49(1), pp. 1… [cited by applicant]
Chen, R., et al., “Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning,” IEEE, 2022, pp. 16123-16134. [cited by applicant]
Choi, H., et al., “S100b and S100b Autoantibody as Biomarkers for Early Detection of Brain Metastases in Lung Cancer,” Translational lung cancer research, 2016, vol. 5(4), pp. 413-419. [cited by applicant]
Coudray, N., et al., Classification and Mutation Prediction From Non-small Cell Lung Cancer Histopathology Images Using Deep Learning, Nature medicine, 2018, vol. 24(10), pp. 1559-1567. [cited by applicant]
Darvas, F., et al., “Mapping Human Brain Function With Meg and Eeg: Methods and Validation,” Neurolmage, 2004, vol. 23, pp. S289-S299. [cited by applicant]
Demene, C., et al., “Transcranial Ultrafast Ultrasound Localization Microscopy of Brain Vasculature in Patients,” Nature biomedical engineering, 2021, vol. 5(3), pp. 219-228. [cited by applicant]
Echle, A., et al., “Deep Learning in Cancer Pathology: a New Generation of Clinical Biomarkers,” British journal of cancer, 2021, vol. 124(4), pp. 686-696. [cited by applicant]
Eggebrecht, A., et al., “Mapping Distributed Brain Function and Networks With Diffuse Optical Tomography,” Nature photonics, 2014, vol. 8(6), pp. 448-454. [cited by applicant]
Fatima A., et al., “Review of Cost Reduction Methods in Photoacoustic Computed Tomography”, Photoacoustics, 2019, vol. 15(100137), pp. 1-12. [cited by applicant]
Felip, E., et al., “Overall Survival with Adjuvant Atezolizumab After Chemotherapy in Resected Stage II-IIIA Non-small-cell Lung Cancer (IMpower010): A Randomised, Multicentre, Open-label, Phase III Trial,” Annals of On… [cited by applicant]
Ganti, A., et al., “Update of Incidence, Prevalence, Survival, and Initial Treatment in Patients With Non-Small Cell Lung Cancer in the US,” JAMA oncology, 2021, vol. 7(12), pp. 1824-1832. [cited by applicant]
Godoy, L., et al., “Emerging Precision Neoadjuvant Systemic Therapy for Patients With Resectable Non-small Cell Lung Cancer: Current Status and Perspectives,” Biomarker research, 2023, vol. 11(1), pp. 1-29. [cited by applicant]
He, K. et al., “deep Residual Learning for Image Recognition”, Microsoft Research, 2015, pp. 1-12 URL: https://arxiv.org/abs/1512.03385v1. [cited by applicant]
International Search Report and Written Opinion dated Aug. 5, 2024 in PCT Application No. PCT/US2024/024868. [cited by applicant]
International Search Report and Written Opinion dated May 9, 2024 in PCT Application No. PCT/US2024/011281. [cited by applicant]
Johnson, B., et al., “Patient Subsets Benefiting From Adjuvant Therapy Following Surgical Resection of Non-small Cell Lung Cancer,” Clinical cancer research, 2005, vol. 11(13), pp. 5022s-5026s. [cited by applicant]
Lai P., et al., “Time-reversed Ultrasonically Encoded Optical Focusing in Biological Tissue,” Journal of Biomedical Optics, 2012, vol. 17 (3), pp. 1-4. [cited by applicant]
Li Z., et al., Broadband Gradient Impedance Matching Using an Acoustic Metamaterial for Ultrasonic Transducers, Scientific Reports, 2017, vol. 7(42863), pp. 1-9. [cited by applicant]
Cited By (2)
US 12,682,620 US 12,687,521