Unsupervised Segmentation of Cervical Cell Nuclei via Adaptive Clustering. - Srishti Gautam; Krati Gupta; Arnav Bhavsar; Anil Kumar Sao (MIUA, 2017)

DeepCerv: Deep Neural Network for Segmentation Free Robust Cervical Cell Classification. - O. U. Nirmal Jith; K. K. Harinarayanan; Srishti Gautam; Arnav Bhavsar; Anil Kumar Sao (COMPAY/OMIA@MICCAI, 2018)

CNN based segmentation of nuclei in PAP-smear images with selective pre-processing. - Srishti Gautam; Arnav Bhavsar; Anil Kumar Sao; Harinarayan K. K. (Digital Pathology, 2018)

Considerations for a PAP Smear Image Analysis System with CNN Features. - Srishti Gautam; Harinarayan K. K.; O. U. Nirmal Jith; Anil Kumar Sao; Arnav Bhavsar; Adarsh Natarajan (CoRR, 2018)

This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation. - Srishti Gautam; Marina M.-C. Höhne; Stine Hansen; Robert Jenssen; Michael Kampffmeyer (CoRR, 2021)

A self-guided anomaly detection-inspired few-shot segmentation network. - Suaiba Amina Salahuddin; Stine Hansen; Srishti Gautam; Michael Kampffmeyer; Robert Jenssen (CVCS, 2022)

Demonstrating the Risk of Imbalanced Datasets in Chest X-Ray Image-Based Diagnostics by Prototypical Relevance Propagation. - Srishti Gautam; Marina M.-C. Höhne; Stine Hansen; Robert Jenssen; Michael Kampffmeyer (ISBI, 2022)

ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model. - Srishti Gautam; Ahcène Boubekki; Stine Hansen; Suaiba Amina Salahuddin; Robert Jenssen; Marina M.-C. Höhne; Michael Kampffmeyer (NeurIPS, 2022)

Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels. - Stine Hansen; Srishti Gautam; Robert Jenssen; Michael Kampffmeyer (Medical Image Anal., 2022)

Investigating the Fairness of Large Language Models for Predictions on Tabular Data. - Yanchen Liu; Srishti Gautam; Jiaqi Ma 004; Himabindu Lakkaraju (CoRR, 2023)

This looks More Like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation. - Srishti Gautam; Marina M.-C. Höhne; Stine Hansen; Robert Jenssen; Michael Kampffmeyer (Pattern Recognit., 2023)

ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement. - Stine Hansen; Srishti Gautam; Suaiba Amina Salahuddin; Michael Kampffmeyer; Robert Jenssen (Medical Image Anal., 2023)

Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications. - Yanchen Liu; Srishti Gautam; Jiaqi Ma 004; Himabindu Lakkaraju (NAACL-HLT, 2024)

Prototypical Self-Explainable Models Without Re-training. - Srishti Gautam; Ahcene Boubekki; Marina M.-C. Höhne; Michael Kampffmeyer (Trans. Mach. Learn. Res., 2024)