Framing attention in American and Japanese comics. - Neil Cohn; Amaro Taylor-Weiner; Suzanne Grossman (CogSci, 2012)

Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology. - Syed Ashar Javed; Dinkar Juyal; Harshith Padigela; Amaro Taylor-Weiner; Limin Yu; Aaditya Prakash (NeurIPS, 2022)

Additive MIL: Intrinsic Interpretability for Pathology. - Syed Ashar Javed; Dinkar Juyal; Harshith Padigela; Amaro Taylor-Weiner; Limin Yu; Aaditya Prakash (CoRR, 2022)

Synthetic DOmain-Targeted Augmentation (S-DOTA) Improves Model Generalization in Digital Pathology. - Sai Chowdary Gullapally; Yibo Zhang; Nitin Kumar Mittal; Deeksha Kartik; Sandhya Srinivasan; Kevin Rose; Daniel Shenker; Dinkar Juyal; Harshith Padigela; Raymond Biju; Victor Minden; Chirag Maheshwari; et al. (CoRR, 2023)

ContriMix: Unsupervised disentanglement of content and attribute for domain generalization in microscopy image analysis. - Tan H. Nguyen; Dinkar Juyal; Jin Li; Aaditya Prakash; Shima Nofallah; Chintan Shah; Sai Chowdary Gullapally; Michael Griffin; Anand Sampat; John Abel; Justin Lee; Amaro Taylor-Weiner (CoRR, 2023)

Improved statistical benchmarking of digital pathology models using pairwise frames evaluation. - Ylaine Gerardin; John Shamshoian; Judy Shen; Nhat Le; Jamie Prezioso; John Abel; Isaac Finberg; Daniel Borders; Raymond Biju; Michael Nercessian; Vaed Prasad; Joseph Lee; et al. (CoRR, 2023)

ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology. - Tan H. Nguyen; Dinkar Juyal; Jin Li; Aaditya Prakash; Shima Nofallah; Chintan Shah; Sai Chowdary Gullapally; Limin Yu; Michael Griffin; Anand Sampat; John Abel; Justin Lee; et al. (COMPAY@MICCAI, 2024)

SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology. - Dinkar Juyal; Siddhant Shingi; Syed Ashar Javed; Harshith Padigela; Chintan Shah; Anand Sampat; Archit Khosla; John Abel; Amaro Taylor-Weiner (WACV, 2024)