Search Results for author:
Found 10 papers, 3 papers with code
Fully Convolutional Neural Network for Semantic Segmentation of Anatomical Structure and Pathologies in Colour Fundus Images Associated with Diabetic Retinopathy
This paper proposes a method for the automated segmentation of retinal lesions and optic disk in fundus images using a deep fully convolutional neural network for semantic segmentation.
Multitask Learning of Temporal Connectionism in Convolutional Networks using a Joint Distribution Loss Function to Simultaneously Identify Tools and Phase in Surgical Videos
Surgical workflow analysis is of importance for understanding onset and persistence of surgical phases and individual tool usage across surgery and in each phase.
Unit Impulse Response as an Explainer of Redundancy in a Deep Convolutional Neural Network
Convolutional neural networks (CNN) are generally designed with a heuristic initialization of network architecture and trained for a certain task.
Adversarially Trained Convolutional Neural Networks for Semantic Segmentation of Ischaemic Stroke Lesion using Multisequence Magnetic Resonance Imaging
In this paper, we present a data-driven fully automated method for estimation of core and penumbra in ischaemic lesions using diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) sequence maps of MRI.
CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0. 98 $\pm$ 0. 00 / 0. 95 $\pm$ 0. 01) but the best MSSD performance remain limited (21. 89 $\pm$ 13. 94 / 20. 85 $\pm$ 10. 63 mm).
Identification of Cervical Pathology using Adversarial Neural Networks
Various screening and diagnostic methods have led to a large reduction of cervical cancer death rates in developed countries.
Lung Segmentation and Nodule Detection in Computed Tomography Scan using a Convolutional Neural Network Trained Adversarially using Turing Test Loss
Lung cancer is the most common form of cancer found worldwide with a high mortality rate.
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge.
Ranked #1 on
Action Triplet Recognition
on CholecT50 (Challenge)
(using extra training data)
CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection.
Ranked #1 on
Action Triplet Detection
on CholecT50 (Challenge)
Task-driven Prompt Evolution for Foundation Models
In this work, we propose a plug-and-play Prompt Optimization Technique for foundation models like SAM (SAMPOT) that utilizes the downstream segmentation task to optimize the human-provided prompt to obtain improved performance.
