| CPC G16H 30/40 (2018.01) [G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G06T 2207/10072 (2013.01); G06T 2207/30056 (2013.01); G06T 2207/30096 (2013.01)] | 32 Claims |

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1. A method for automatically processing 3D images of a subject to identify and/or characterize cancerous lesions within the subject, the method comprising:
(a) receiving, by a processor of a computing device, a 3D functional image of the subject obtained using a functional imaging modality;
(b) automatically detecting, by the processor, using a machine learning module, one or more hotspots within the 3D functional image, each hotspot corresponding to a local region of elevated intensity with respect to its surrounding and representing a potential cancerous lesion within the subject, thereby creating a 3D hotspot map, identifying, for each hotspot, a corresponding 3D hotspot volume within the 3D functional image, wherein the machine learning module receives, as input, at least a portion of the 3D functional image and generates the 3D hotspot map as output; and
(c) storing and/or providing, for display and/or further processing, the 3D hotspot map,
wherein the 3D functional image comprises a positron emission tomography (PET) or a single-photon emission computed tomography (SPECT) image obtained following administration of an agent to the subject.
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