activity
20162023
most citedMonte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification

283 citations · 1.1k across the 29 of their papers we have counts for

collaborators

53 papers

eess.IV2023

Self-Knowledge Distillation for Surgical Phase Recognition

Jinglu Zhang, Santiago Barbarisi, Abdolrahim Kadkhodamohammadi +2

Purpose: Advances in surgical phase recognition are generally led by training deeper networks. Rather than going further with a more complex solution, we believe that current model…

cs.CV2023

A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation

Ronast Subedi, Rebati Raman Gaire, Sharib Ali +3

This paper presents a solution to the cross-domain adaptation problem for 2D surgical image segmentation, explicitly considering the privacy protection of distributed datasets belo…

cs.CV2022

Objective Surgical Skills Assessment and Tool Localization: Results from the MICCAI 2021 SimSurgSkill Challenge

Aneeq Zia, Kiran Bhattacharyya, Xi Liu +12

Timely and effective feedback within surgical training plays a critical role in developing the skills required to perform safe and efficient surgery. Feedback from expert surgeons,…

cs.CV2022

Generalized Product-of-Experts for Learning Multimodal Representations in Noisy Environments

Abhinav Joshi, Naman Gupta, Jinang Shah +3

A real-world application or setting involves interaction between different modalities (e.g., video, speech, text). In order to process the multimodal information automatically and…

cs.CV20222 cited

Task-Aware Active Learning for Endoscopic Image Analysis

Shrawan Kumar Thapa, Pranav Poudel, Binod Bhattarai +1

Semantic segmentation of polyps and depth estimation are two important research problems in endoscopic image analysis. One of the main obstacles to conduct research on these resear…

eess.IV20223 cited

Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentation

Binod Bhattarai, Ronast Subedi, Rebati Raman Gaire +2

We present our novel deep multi-task learning method for medical image segmentation. Existing multi-task methods demand ground truth annotations for both the primary and auxiliary…