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The needs and desires of lesbians and gay men with cancer at the end of their lives are not fundamentally different from any other dying individual's needs. There are, however, significant legal restrictions and societal attitudes that can negatively affect the dying experiences of lesbians and gay men. Lesbians and gay men face many challenges at the end of their lives, including issues of disclosure in the healthcare setting, discrimination, misconceptions, legal and financial barriers and the disenfranchised grief of surviving same-sex partners. Oncology social workers can play a prominent role in advocating for these individuals to allow for dignity and support in spite of these barriers and provide effective interventions to help in assisting lesbians and gaymen with healthcare decision-making and end-of-life care planning.

Facial expressions distort visual cues for identification in two-dimensional images. Face processing systems in the brain must decouple image-based information from multiple sources to operate in the social world. Deep convolutional neural networks (DCNN) trained for face identification retain identity-irrelevant, image-based information (e.g., viewpoint). We asked whether a DCNN trained for identity also retains expression information that generalizes over viewpoint change. DCNN representations were generated for a controlled dataset containing images of 70 actors posing 7 facial expressions (happy, sad, angry, surprised, fearful, disgusted, neutral), from 5 viewpoints (frontal, 90° and 45° left and right profiles). Two-dimensional visualizations of the DCNN representations revealed hierarchical groupings by identity, followed by viewpoint, and then by facial expression. Linear discriminant analysis of full-dimensional representations predicted expressions accurately, mean 76.8% correct for happiness, followed by surprise, disgust, anger, neutral, sad, and fearful at 42.0%; chance 14.3%. Expression classification was stable across viewpoints. Representational similarity heatmaps indicated that image similarities within identities varied more by viewpoint than by expression. We conclude that an identity-trained, deep network retains shape-deformable information about expression and viewpoint, along with identity, in a unified form—consistent with a recent hypothesis for ventral visual stream processing.