activity
20222024
most citedA Data-Efficient Deep Learning Framework for Segmentation and Classification of Histopathology Images

1 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2024

Exploring Intrinsic Properties of Medical Images for Self-Supervised Binary Semantic Segmentation

Pranav Singh, Jacopo Cirrone

Recent advancements in self-supervised learning have unlocked the potential to harness unlabeled data for auxiliary tasks, facilitating the learning of beneficial priors. This has…

cs.CV20241 cited

Free Form Medical Visual Question Answering in Radiology

Abhishek Narayanan, Rushabh Musthyala, Rahul Sankar +3

Visual Question Answering (VQA) in the medical domain presents a unique, interdisciplinary challenge, combining fields such as Computer Vision, Natural Language Processing, and Kno…

eess.IV20231 cited

Enhancing Medical Image Segmentation: Optimizing Cross-Entropy Weights and Post-Processing with Autoencoders

Pranav Singh, Luoyao Chen, Mei Chen +4

The task of medical image segmentation presents unique challenges, necessitating both localized and holistic semantic understanding to accurately delineate areas of interest, such…

cs.LG20231 cited

Efficient Representation Learning for Healthcare with Cross-Architectural Self-Supervision

Pranav Singh, Jacopo Cirrone

In healthcare and biomedical applications, extreme computational requirements pose a significant barrier to adopting representation learning. Representation learning can enhance th…

cs.CV2023

Cross-Architectural Positive Pairs improve the effectiveness of Self-Supervised Learning

Pranav Singh, Jacopo Cirrone

Existing self-supervised techniques have extreme computational requirements and suffer a substantial drop in performance with a reduction in batch size or pretraining epochs. This…

eess.IV20221 cited

A Data-Efficient Deep Learning Framework for Segmentation and Classification of Histopathology Images

Pranav Singh, Jacopo Cirrone

The current study of cell architecture of inflammation in histopathology images commonly performed for diagnosis and research purposes excludes a lot of information available on th…