53 citations · 93 across the 30 of their papers we have counts for
27 papers · 1 filter
DART: Depth-as-Target Pretraining for Surgical Vision Foundation Models
John J. Han, Adam Schmidt, Muhammad Abdullah Jamal +2
Vision foundation models (VFMs) are valuable in data-scarce domains such as surgery, where a single pretrained backbone can provide rich representations for many downstream tasks.…
Point Tracking in Surgery--The 2025 Surgical Tattoos in Infrared Challenge (STIRC2025)
Adam Schmidt, Mert Asim Karaoglu, Zijian Wu +32
Point tracking in surgery is crucial to enable applications in downstream tasks such as segmentation, 3D reconstruction, virtual tissue landmarking, autonomous probe-based scanning…
Track2Map: Online Deformable SLAM with Motion-Aware Pose Optimization in Robotic Surgery
Tianyi Song, Sierra Bonilla, Xinwei Ju +6
Gaussian splatting is the current state-of-the-art for dense, deformable 3D anatomy reconstruction in robot-assisted minimally invasive surgery (RAMIS); however, most pipelines are…
SCARED-C: Corrected Camera Poses for Endoscopic Depth Estimation
John J. Han, Adam Schmidt, Max Allan +2
The SCARED dataset is a widely used benchmark for endoscopic depth estimation, offering ground-truth 3D reconstructions captured with a structured light sensor. However, the depth…
SUREON: A Benchmark and Vision-Language-Model for Surgical Reasoning
Alejandra Perez, Anita Rau, Lee White +4
Surgeons don't just see -- they interpret. When an expert observes a surgical scene, they understand not only what instrument is being used, but why it was chosen, what risk it pos…
On the Role of Depth in Surgical Vision Foundation Models: An Empirical Study of RGB-D Pre-training
John J. Han, Adam Schmidt, Muhammad Abdullah Jamal +4
Vision foundation models (VFMs) have emerged as powerful tools for surgical scene understanding. However, current approaches predominantly rely on unimodal RGB pre-training, overlo…
SurgLaVi: Large-Scale Hierarchical Dataset for Surgical Vision-Language Representation Learning
Alejandra Perez, Chinedu Nwoye, Ramtin Raji Kermani +2
Vision-language pre-training (VLP) offers unique advantages for surgery by aligning language with surgical videos, enabling workflow understanding and transfer across tasks without…
Point Tracking in Surgery--The 2024 Surgical Tattoos in Infrared (STIR) Challenge
Adam Schmidt, Mert Asim Karaoglu, Soham Sinha +37
Understanding tissue motion in surgery is crucial to enable applications in downstream tasks such as segmentation, 3D reconstruction, virtual tissue landmarking, autonomous probe-b…
Multi-view Video-Pose Pretraining for Operating Room Surgical Activity Recognition
Idris Hamoud, Vinkle Srivastav, Muhammad Abdullah Jamal +3
Understanding the workflow of surgical procedures in complex operating rooms requires a deep understanding of the interactions between clinicians and their environment. Surgical ac…
A Two-Stage Progressive Pre-training using Multi-Modal Contrastive Masked Autoencoders
Muhammad Abdullah Jamal, Omid Mohareri
In this paper, we propose a new progressive pre-training method for image understanding tasks which leverages RGB-D datasets. The method utilizes Multi-Modal Contrastive Masked Aut…
VidLPRO: A eo-anguage re-training Framework for botic and Laparoscopic Surgery
Mohammadmahdi Honarmand, Muhammad Abdullah Jamal, Omid Mohareri
We introduce VidLPRO, a novel video-language (VL) pre-training framework designed specifically for robotic and laparoscopic surgery. While existing surgical VL models primarily rel…
Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets
Muhammad Abdullah Jamal, Omid Mohareri
Surgical scene understanding is a key technical component for enabling intelligent and context aware systems that can transform various aspects of surgical interventions. In this w…
Self-supervised Learning via Cluster Distance Prediction for Operating Room Context Awareness
Idris Hamoud, Alexandros Karargyris, Aidean Sharghi +2
Semantic segmentation and activity classification are key components to creating intelligent surgical systems able to understand and assist clinical workflow. In the Operating Room…
AdaEmbed: Semi-supervised Domain Adaptation in the Embedding Space
Ali Mottaghi, Mohammad Abdullah Jamal, Serena Yeung +1
Semi-supervised domain adaptation (SSDA) presents a critical hurdle in computer vision, especially given the frequent scarcity of labeled data in real-world settings. This scarcity…
SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge
Dimitrios Psychogyios, Emanuele Colleoni, Beatrice Van Amsterdam +47
Surgical tool segmentation and action recognition are fundamental building blocks in many computer-assisted intervention applications, ranging from surgical skills assessment to de…
ST(OR)2: Spatio-Temporal Object Level Reasoning for Activity Recognition in the Operating Room
Idris Hamoud, Muhammad Abdullah Jamal, Vinkle Srivastav +3
Surgical robotics holds much promise for improving patient safety and clinician experience in the Operating Room (OR). However, it also comes with new challenges, requiring strong…
Tracking and Mapping in Medical Computer Vision: A Review
Adam Schmidt, Omid Mohareri, Simon DiMaio +2
As computer vision algorithms increase in capability, their applications in clinical systems will become more pervasive. These applications include: diagnostics, such as colonoscop…
M3D: Learning 3D priors using Multi-Modal Masked Autoencoders for 2D image and video understanding
Muhammad Abdullah Jamal, Omid Mohareri
We present a new pre-training strategy called M3D (ulti-odal asked ) built based on Multi-modal masked autoencode…
Surgical Tattoos in Infrared: A Dataset for Quantifying Tissue Tracking and Mapping
Adam Schmidt, Omid Mohareri, Simon DiMaio +1
Quantifying performance of methods for tracking and mapping tissue in endoscopic environments is essential for enabling image guidance and automation of medical interventions and s…
SurgMAE: Masked Autoencoders for Long Surgical Video Analysis
Muhammad Abdullah Jamal, Omid Mohareri
There has been a growing interest in using deep learning models for processing long surgical videos, in order to automatically detect clinical/operational activities and extract me…
SENDD: Sparse Efficient Neural Depth and Deformation for Tissue Tracking
Adam Schmidt, Omid Mohareri, Simon DiMaio +1
Deformable tracking and real-time estimation of 3D tissue motion is essential to enable automation and image guidance applications in robotically assisted surgery. Our model, Spars…
Multi-Modal Unsupervised Pre-Training for Surgical Operating Room Workflow Analysis
Muhammad Abdullah Jamal, Omid Mohareri
Data-driven approaches to assist operating room (OR) workflow analysis depend on large curated datasets that are time consuming and expensive to collect. On the other hand, we see…
Adaptation of Surgical Activity Recognition Models Across Operating Rooms
Ali Mottaghi, Aidean Sharghi, Serena Yeung +1
Automatic surgical activity recognition enables more intelligent surgical devices and a more efficient workflow. Integration of such technology in new operating rooms has the poten…
An Empirical Study on Activity Recognition in Long Surgical Videos
Zhuohong He, Ali Mottaghi, Aidean Sharghi +2
Activity recognition in surgical videos is a key research area for developing next-generation devices and workflow monitoring systems. Since surgeries are long processes with highl…