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Idit Diamant

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3 papers

2 author rows

ICML Conference 2025 Conference Paper

Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo

Idan Achituve
Hai Victor Habi
Amir Rosenfeld
Arnon Netzer
Idit Diamant
Ethan Fetaya

In image processing, solving inverse problems is the task of finding plausible reconstructions of an image that was corrupted by some (usually known) degradation operator. Commonly, this process is done using a generative image model that can guide the reconstruction towards solutions that appear natural. The success of diffusion models over the last few years has made them a leading candidate for this task. However, the sequential nature of diffusion models makes this conditional sampling process challenging. Furthermore, since diffusion models are often defined in the latent space of an autoencoder, the encoder-decoder transformations introduce additional difficulties. To address these challenges, we suggest a novel sampling method based on sequential Monte Carlo (SMC) in the latent space of diffusion models. We name our method LD-SMC. We define a generative model for the data using additional auxiliary observations and perform posterior inference with SMC sampling based on a backward diffusion process. Empirical evaluations on ImageNet and FFHQ show the benefits of LD-SMC over competing methods in various inverse problem tasks and especially in challenging inpainting tasks.

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ICML Conference 2024 Conference Paper

Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning

Idan Achituve
Idit Diamant
Arnon Netzer
Gal Chechik
Ethan Fetaya

As machine learning becomes more prominent there is a growing demand to perform several inference tasks in parallel. Multi-task learning (MTL) addresses this challenge by learning a single model that solves several tasks simultaneously and efficiently. Often optimizing MTL models entails first computing the gradient of the loss for each task, and then aggregating all the gradients to obtain a combined update direction. However, common methods following this approach do not consider an important aspect, the sensitivity in the dimensions of the gradients. Some dimensions may be more lenient for changes while others may be more restrictive. Here, we introduce a novel gradient aggregation procedure using Bayesian inference. We place a probability distribution over the task-specific parameters, which in turn induce a distribution over the gradients of the tasks. This valuable information allows us to quantify the uncertainty associated with each of the gradients’ dimensions which is factored in when aggregating them. We empirically demonstrate the benefits of our approach in a variety of datasets, achieving state-of-the-art performance.

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JBHI Journal 2016 Journal Article

Improved Patch-Based Automated Liver Lesion Classification by Separate Analysis of the Interior and Boundary Regions

Idit Diamant
Assaf Hoogi
Christopher F. Beaulieu
Mustafa Safdari
Eyal Klang
Michal Amitai
Hayit Greenspan
Daniel L. Rubin

The bag-of-visual-words (BoVW) method with construction of a single dictionary of visual words has been used previously for a variety of classification tasks in medical imaging, including the diagnosis of liver lesions. In this paper, we describe a novel method for automated diagnosis of liver lesions in portal-phase computed tomography (CT) images that improves over single-dictionary BoVW methods by using an image patch representation of the interior and boundary regions of the lesions. Our approach captures characteristics of the lesion margin and of the lesion interior by creating two separate dictionaries for the margin and the interior regions of lesions (“dual dictionaries” of visual words). Based on these dictionaries, visual word histograms are generated for each region of interest within the lesion and its margin. For validation of our approach, we used two datasets from two different institutions, containing CT images of 194 liver lesions (61 cysts, 80 metastasis, and 53 hemangiomas). The final diagnosis of each lesion was established by radiologists. The classification accuracy for the images from the two institutions was 99% and 88%, respectively, and 93% for a combined dataset. Our new BoVW approach that uses dual dictionaries shows promising results. We believe the benefits of our approach may generalize to other application domains within radiology.

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